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pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"},"responsivegrid":{"columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnCount":12,":items":{"quickstart_hero":{"id":"quickstart-hero-2490688938","isDeveloperGuidesPage":false,"quickstartHeroFirstCertifiedTag":{"tagText":"Quickstart","tagColor":"#29B5E8","tagPath":"/content/cq:tags/snowflake-site/taxonomy/solution-center/certification/quickstart","tagIcon":""},"quickstartHeroTitle":{"lines":["Prompt Engineering and Evaluation of LLM 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-->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EThis quickstart will cover the basics of prompt engineering on your Large Language Models (LLMs) and how to evaluate the responses of different LLMs through human feedback.\u003C/p\u003E\n","\u003Cp\u003EBy completing this guide, you will learn how to run AI experimentation with different LLMs for your use case. First part of the quickstart focusses on using prompt engineering to generate different model responses by tweaking your prompts. The second part focusses on evaluating different LLM model responses through human feedback.\u003C/p\u003E\n","\u003Cp\u003EHere is a summary of what you will be able to learn in each step following this quickstart:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ESetup Environment\u003C/strong\u003E: Setup your Snowflake Free Trial environment\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESnowflake Marketplace\u003C/strong\u003E: Download the data you need from Snowflake Marketplace and use it in your analysis\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESnowflake External Access\u003C/strong\u003E: Integrate LLMs such as GPT-3.5 and GPT4 to Snowflake using External Access\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPrompt Engineering\u003C/strong\u003E: Use different prompts on an LLM and capture the responses into a Snowflake table\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ELLM Evaluation with Human Feedback\u003C/strong\u003E: Build a Streamlit App to compare and rate the LLM responses and capture the ratings in a Snowflake table\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EWhat's next?\u003C/strong\u003E: What other applications can you build for your industry and data, inspired from this quickstart?\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ELet's dive into the key features and technologies used in the demo, for better understanding.\u003C/p\u003E\n","\u003Ch3\u003EKey features & technology\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ELarge language models (LLMs)\u003C/li\u003E\u003Cli\u003EPrompt Engineering\u003C/li\u003E\u003Cli\u003ELLM Evaluation\u003C/li\u003E\u003Cli\u003ESnowflake External Access\u003C/li\u003E\u003Cli\u003ESnowflake Marketplace\u003C/li\u003E\u003Cli\u003EStreamlit\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat is a large language model (LLM)?\u003C/h3\u003E\n","\u003Cp\u003EA large language model, or LLM, is a deep learning algorithm that can recognize, summarize, translate, predict and generate text and other content based on knowledge gained from massive datasets. Some examples of popular LLMs are \u003Ca href=\"https://openai.com/research/gpt-4\"\u003EGPT-4\u003C/a\u003E, \u003Ca href=\"https://openai.com/blog/gpt-3-apps\"\u003EGPT-3\u003C/a\u003E, \u003Ca href=\"https://cloud.google.com/ai-platform/training/docs/algorithms/bert-start\"\u003EBERT\u003C/a\u003E, \u003Ca href=\"https://ai.facebook.com/blog/large-language-model-llama-meta-ai/\"\u003ELLaMA\u003C/a\u003E, and \u003Ca href=\"https://blog.google/technology/ai/lamda/\"\u003ELaMDA\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003EWhat is OpenAI?\u003C/h3\u003E\n","\u003Cp\u003EOpenAI is the AI research and deployment company behind ChatGPT, GPT-4 (and its predecessors), DALL-E, and other notable offerings. Learn more about \u003Ca href=\"https://openai.com/\"\u003EOpenAI\u003C/a\u003E. We use OpenAI in this guide, but you are welcome to use the large language model of your choice in its place.\u003C/p\u003E\n","\u003Ch3\u003EWhat is Prompt Engineering?\u003C/h3\u003E\n","\u003Cp\u003EA prompt is a natural language text that requests the Generative AI model to perform a specific task. Prompt Engineering is the process of guiding the AI model to generate a specific response by tweaking the prompt text. Prompt Engineering uses a mix of creativity and experimentation to identify the right set of prompts that ensures the AI model will return the desired response.\u003C/p\u003E\n","\u003Ch3\u003EWhat is LLM Evaluation?\u003C/h3\u003E\n","\u003Cp\u003ESince Large Language Model outputs free form texts that may not have the ground truth, it can be difficult to evaluate the LLMs using traditional model metrics such as Accuracy, Precision, Recall, F1-Score, etc. There are multiple frameworks in development to evaluate the bias, toxicity and hallucination in LLM responses. However, the most popular and efficient method has been human-in-the-loop evaluation. That is, letting humans rate the model responses as helpful and right.\u003C/p\u003E\n","\u003Ch3\u003EWhat is Snowflake External Access?\u003C/h3\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/external-network-access/external-network-access-overview\"\u003EExternal Access\u003C/a\u003E helps to securely connect to the OpenAI API from \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark/index\"\u003ESnowpark\u003C/a\u003E, the set of libraries and runtimes in Snowflake that run non-SQL code, including Python, Java and Scala. External Access provides flexibility to reach public internet endpoints from the Snowpark sandbox without any additional infrastructure setup.\u003C/p\u003E\n","\u003Ch3\u003EWhat is the Snowflake Marketplace?\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Ca href=\"/en/data-cloud/marketplace/\"\u003ESnowflake Marketplace\u003C/a\u003E provides users with access to a wide range of datasets from third-party data stewards, expanding the data available for transforming business processes and making decisions. Data providers can publish datasets and offer data analytics services to Snowflake customers. Customers can securely access shared datasets directly from their Snowflake accounts and receive automatic real-time updates.\u003C/p\u003E\n","\u003Ch3\u003EWhat is Streamlit?\u003C/h3\u003E\n","\u003Cp\u003EStreamlit enables data scientists and Python developers to combine Streamlit's component-rich, open-source Python library with the scale, performance, and security of the Snowflake platform. Learn more about \u003Ca href=\"https://streamlit.io/\"\u003EStreamlit\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Will Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to access data from Snowflake Marketplace and use it for your analysis\u003C/li\u003E\u003Cli\u003EHow to use External Access to securely connect to the OpenAI API from Snowpark\u003C/li\u003E\u003Cli\u003EHow to use different prompts on a Large Language Model and capture model responses in a Snowflake table\u003C/li\u003E\u003Cli\u003EHow to build a Streamlit App to compare the model responses and capture the review in a Snowflake table\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA \u003Cstrong\u003ESnowflake account\u003C/strong\u003E with ACCOUNTADMIN role access - \u003Ca href=\"https://signup.snowflake.com/?utm_cta=quickstarts_\"\u003ESign-in or create a free trial account\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAnaconda Terms & Conditions accepted\u003C/strong\u003E. See Getting Started section in \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-packages.html#getting-started\"\u003EThird-Party Packages\u003C/a\u003E.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EGitHub account\u003C/strong\u003E - If you don't already have a GitHub account you can create one for free. Visit the \u003Ca href=\"https://github.com/signup\"\u003EJoin GitHub\u003C/a\u003E page to get started.\u003C/li\u003E\u003Cli\u003EA \u003Cstrong\u003EOpenAI account\u003C/strong\u003E or API key to another language model - \u003Ca href=\"https://openai.com/\"\u003ESign-in or create an account\u003C/a\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://platform.openai.com/account/api-keys\"\u003EOpenAI API Key\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESet up Snowflake\u003C/h2\u003E\n","\u003Cp\u003ESign up for \u003Ca href=\"https://signup.snowflake.com/?utm_cta=quickstarts_\"\u003ESnowflake Free Trial\u003C/a\u003E and create an account. Log into \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight.html#\"\u003ESnowsight\u003C/a\u003E using your credentials to create tables, streamlit app and more.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\nIMPORTANT:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EIf you use different names for objects created in this section, be sure to update scripts and code in the following sections accordingly.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EFor each SQL script block below, select all the statements in the block and execute them top to bottom.\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EClone GitHub Repository\u003C/h3\u003E\n","\u003Cp\u003EThe very first step is to clone the \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-prompt-engineering-and-llm-evaluation\"\u003EGitHub repository\u003C/a\u003E. This repository contains all the code you will need to successfully complete this QuickStart Guide.\u003C/p\u003E\n","\u003Cp\u003EUsing HTTPS:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-shell\"\u003Egit clone https://github.com/Snowflake-Labs/sfguide-prompt-engineering-and-llm-evaluation.git\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOR, using SSH:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-shell\"\u003Egit clone git@github.com:Snowflake-Labs/sfguide-prompt-engineering-and-llm-evaluation.git\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate Databases, Tables and Warehouses\u003C/h3\u003E\n","\u003Cp\u003ERun the following SQL commands to create the \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-warehouse.html\"\u003Ewarehouse\u003C/a\u003E, \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-database.html\"\u003Edatabase\u003C/a\u003E and \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-schema.html\"\u003Eschema\u003C/a\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE DATABASE CUSTOMER_EXP_DB;\nCREATE OR REPLACE SCHEMA CUSTOMER_EXP_SCHEMA;\nCREATE OR REPLACE WAREHOUSE VINO_CUSTOMER_EXP_WH_M WAREHOUSE_SIZE='MEDIUM';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ERun the following commands to change the scope to the database, schema and warehouse created above.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE DATABASE CUSTOMER_EXP_DB;\nUSE SCHEMA CUSTOMER_EXP_SCHEMA;\nUSE WAREHOUSE VINO_CUSTOMER_EXP_WH_M;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESnowflake Marketplace\u003C/h2\u003E\n","\u003Cp\u003EDuring this step, we will be loading customer experience data to Snowflake. But "loading" is really the wrong word here. Because we're using Snowflake's unique data sharing capability we don't actually need to copy the data to our Snowflake account with a custom ETL process. Instead we can directly access the customer review data shared by \u003Ccode\u003EClootrack\u003C/code\u003E in the Snowflake Marketplace.\u003C/p\u003E\n","\u003Ch3\u003ECustomer Experience data from Snowflake Marketplace\u003C/h3\u003E\n","\u003Cp\u003ELet's connect to the data from Snowflake Marketplace by following these steps:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ELog into Snowsight\u003C/li\u003E\u003Cli\u003EClick on the \u003Ccode\u003EMarketplace\u003C/code\u003E tab in the left navigation bar\u003C/li\u003E\u003Cli\u003EEnter \u003Ccode\u003ECustomer Experience Score by Product\u003C/code\u003E in the search box and click return\u003C/li\u003E\u003Cli\u003EClick on the \u003Ccode\u003ESample - USA\u003C/code\u003E listing title\u003C/li\u003E\u003Cli\u003EClick the blue \u003Ccode\u003EGet\u003C/code\u003E button\n\u003Cul\u003E\u003Cli\u003EExpand the \u003Ccode\u003EOptions\u003C/code\u003E dialog\u003C/li\u003E\u003Cli\u003EChange the database name to read \u003Ccode\u003ECUSTOMER_EXP\u003C/code\u003E (all capital letters)\u003C/li\u003E\u003Cli\u003ESelect the \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E role to have access to the new database\u003C/li\u003E\u003Cli\u003EClick the blue \u003Ccode\u003EGet\u003C/code\u003E button\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n\u003Chr\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/prompt-engineering-and-llm-evaluation/marketplace.png\" alt=\"Snowflake Marketplace\"\u003E\u003C/p\u003E\n\u003Chr\u003E\n","\u003Cp\u003EThat's it... we have the dataset to work with.\u003C/p\u003E\n","\u003Ch3\u003EQuery the Marketplace data\u003C/h3\u003E\n","\u003Cp\u003ERun the following queries to peek into the data we got from the Marketplace.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT COUNT(*) as row_count \nFROM CUSTOMER_EXP.PUBLIC.TRIAL_PRODUCT_CUSTOMER_EXPERIENCE_VIEW;\n\nSELECT * \nFROM CUSTOMER_EXP.PUBLIC.TRIAL_PRODUCT_CUSTOMER_EXPERIENCE_VIEW\nLIMIT 5;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou can also view the shared database \u003Ccode\u003ECUSTOMER_EXP.PUBLIC.TRIAL_PRODUCT_CUSTOMER_EXPERIENCE_VIEW\u003C/code\u003E by navigating to the Snowsight UI -> Data -> Databases.\u003C/p\u003E\n","\u003Ch3\u003ECopy the Marketplace data into our Database\u003C/h3\u003E\n","\u003Cp\u003EThe database and the view we got from the Marketplace are read-only. In order to be able to write data into the table, we need to copy \u003Ccode\u003ECUSTOMER_EXP.PUBLIC.TRIAL_PRODUCT_CUSTOMER_EXPERIENCE_VIEW\u003C/code\u003E view into another table.\u003C/p\u003E\n","\u003Cp\u003ERun the following SQL command to create a new table and copy data into the new table\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE TABLE CUSTOMER_EXP_REVIEWS (\n    brand_name VARCHAR(100),\n    product_name VARCHAR(1000),\n    sub_category VARCHAR(100),\n    positive_customer_exp NUMBER(10,2),\n    sentence_count NUMBER(10),\n    month STRING(15),\n    year NUMBER(4),\n    start_date DATE,\n    end_date DATE\n);\n\nINSERT INTO CUSTOMER_EXP_REVIEWS\nSELECT * \nFROM CUSTOMER_EXP.PUBLIC.TRIAL_PRODUCT_CUSTOMER_EXPERIENCE_VIEW;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ELet us quickly review the data from the newly created table\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT COUNT(*) \nFROM CUSTOMER_EXP_REVIEWS;\n\nSELECT *\nFROM CUSTOMER_EXP_REVIEWS\nLIMIT 5;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe table has several columns but the \u003Ccode\u003Eproduct_name\u003C/code\u003E column will be of interest to us. What if we used the LLMs to create a detailed product description from the product_name column? Let's do it.\u003C/p\u003E\n","\u003Cp\u003ELet us create a new table called \u003Ccode\u003EPRODUCT\u003C/code\u003E to store the unique product names from the CUSTOMER_EXP_REVIEWS table.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE TABLE PRODUCT \nAS\nSELECT DISTINCT product_name\nFROM CUSTOMER_EXP_REVIEWS\nLIMIT 10;\n\nSELECT COUNT(*)\nFROM PRODUCT;\n\nSELECT * \nFROM PRODUCT;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\nIMPORTANT:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EIf you used a different name for the database while getting weather data from the marketplace, be sure to update scripts and code in the following sections accordingly.\u003C/li\u003E\u003C/ul\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EUse External Access\u003C/h2\u003E\n","\u003Cp\u003EIn this implementation, we will use External Access to securely connect to the OpenAI API from Snowpark Python.\u003C/p\u003E\n","\u003Ch3\u003EStep 1\u003C/h3\u003E\n","\u003Cp\u003ECreate a \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-secret\"\u003ESecret\u003C/a\u003E object in Snowflake to securely store your OpenAI API key.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE SECRET vino_open_ai_api\n TYPE = GENERIC_STRING\n SECRET_STRING = 'YOUR-OPENAI-API-KEY';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 2\u003C/h3\u003E\n","\u003Cp\u003ECreate a \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-network-rule\"\u003ENetwork Rule\u003C/a\u003E object in Snowflake.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE NETWORK RULE vino_apis_network_rule\n MODE = EGRESS\n TYPE = HOST_PORT\n VALUE_LIST = ('api.openai.com');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 3\u003C/h3\u003E\n","\u003Cp\u003ECreate a \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-external-access-integration\"\u003EExternal Access Integration\u003C/a\u003E object in Snowflake.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE EXTERNAL ACCESS INTEGRATION vino_external_access_int\n ALLOWED_NETWORK_RULES = (vino_apis_network_rule)\n ALLOWED_AUTHENTICATION_SECRETS = (vino_open_ai_api)\n ENABLED = true;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 4\u003C/h3\u003E\n","\u003Cp\u003ECreate Snowpark Python function in Snowflake. Let us understand what this SQL query does:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EIt uses Python 3.9 runtime\u003C/li\u003E\u003Cli\u003EIt uses the package \u003Ccode\u003Eopenai\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EIt uses the external access integration we created in the above step\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Ecomplete_me()\u003C/code\u003E function takes the query we pass, the prompt we have defined and invokes ChatCompletion API of OpenAI's \u003Ccode\u003Egpt-3.5-turbo\u003C/code\u003E model\u003C/li\u003E\u003Cli\u003EIt returns the model response in the end\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE FUNCTION PROD_DESC_CHATGPT35_V1(query varchar)\nRETURNS STRING\nLANGUAGE PYTHON\nRUNTIME_VERSION = 3.9\nHANDLER = 'complete_me'\nEXTERNAL_ACCESS_INTEGRATIONS = (vino_external_access_int)\nSECRETS = ('openai_key' = vino_open_ai_api)\nPACKAGES = ('openai')\nAS\n$$\nimport _snowflake\nimport openai\nopenai.api_key = _snowflake.get_generic_secret_string('openai_key')\nmodel="gpt-3.5-turbo"\nprompt="Explain in details what the ingredients and capabilities of this cosmetic product are. Limit the response to 150 words only. Here is the product:"\ndef complete_me(QUERY):\n    messages=[\n    {'role': 'user', 'content':f"{prompt} {QUERY}"}\n    ]\n    response = openai.ChatCompletion.create(model=model,messages=messages,temperature=0)    \n    return response.choices[0].message["content"]\n$$;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003E\u003Cstrong\u003ECall Function in Snowflake\u003C/strong\u003E\u003C/h3\u003E\n","\u003Cp\u003EAssuming you have completed all the prerequites, calling the function is as simple as follow:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT PROD_DESC_CHATGPT35_V1('Magnesium Lotion With Aloe Vera, Shea Butter, Coconut Oil & Magnesium Oil For Muscle Pain & Leg Cramps – Rich In Magnesium Chloride And Vitamin E Oil') as response;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cem\u003E\u003Cstrong\u003ESample response:\u003C/strong\u003E\u003C/em\u003E \u003Cem\u003E"This cosmetic product, the Magnesium Lotion with Aloe Vera, Shea Butter, Coconut Oil, and Magnesium Oil, is specifically designed to alleviate muscle pain and leg cramps. It contains a combination of key ingredients that work synergistically to provide relief and nourishment to the skin.The lotion is rich in Magnesium Chloride, a mineral known for its muscle relaxing properties. Magnesium Chloride helps to reduce muscle tension, ease cramps, and promote relaxation. Additionally, it aids in the absorption of magnesium into the body, which is essential for maintaining healthy muscle function.Aloe Vera is included in the formula for its soothing and moisturizing properties. It helps to calm irritated skin, reduce inflammation, and provide hydration.Shea Butter and Coconut Oil are both deeply moisturizing ingredients that help to nourish and soften the skin. They create a protective barrier that locks in moisture, leaving the skin feeling smooth and supple.Vitamin E Oil is added for its antioxidant properties, which help to protect the skin from free radicals and environmental damage.Overall, this Magnesium Lotion combines the benefits of Magnesium Chloride, Aloe Vera, Shea Butter, Coconut Oil, and Vitamin E Oil to provide relief from muscle pain and leg cramps while simultaneously nourishing and hydrating the skin."\u003C/em\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT PROD_DESC_CHATGPT35_V1('Tandoori Mixed Grill') as response;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cem\u003E\u003Cstrong\u003ESample response:\u003C/strong\u003E\u003C/em\u003E \u003Cem\u003E"Tandoori Mixed Grill is not a cosmetic product, but rather a popular Indian dish consisting of a variety of marinated meats and vegetables cooked in a traditional clay oven called a tandoor. It typically includes ingredients such as chicken, lamb, fish, or shrimp, along with a blend of spices and yogurt-based marinade. The marinade is made using a combination of ingredients like ginger, garlic, turmeric, cumin, coriander, paprika, garam masala, and yogurt, which help to tenderize the meat and infuse it with rich flavors. The capabilities of Tandoori Mixed Grill lie in its ability to provide a delicious and aromatic culinary experience. The combination of spices and yogurt-based marinade not only enhances the taste of the meats and vegetables but also helps to tenderize and moisten them during the cooking process. The high heat of the tandoor oven imparts a smoky and charred flavor to the dish, giving it a unique and authentic taste. Tandoori Mixed Grill is often served with naan bread, rice, or a side salad, making it a satisfying and flavorful meal option."\u003C/em\u003E\u003C/p\u003E\n","\u003Cp\u003EVery cool. Isn't it? Now we are able to call gpt-3.5-turbo model APIs from Snowflake. You can also use \u003Ccode\u003EGPT\u003C/code\u003E model on your Snowflake data.\u003C/p\u003E\n","\u003Cp\u003ENow that we are able to access OpenAI's gpt model from Snowflake, let's go ahead and try out the Snowpark \u003Ccode\u003EPROD_DESC_CHATGPT35_V1\u003C/code\u003E function on our Snowflake data.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EPrompt Engineering\u003C/h2\u003E\n","\u003Cp\u003ESo far, we created a snowpark function to invoke the OpenAI's ChatCompletion APIs. We got access to the Customer Experience data from Marketplace. In this step, let us use the OpenAI APIs to generate detailed product descriptions for the products in our table.\u003C/p\u003E\n","\u003Ch3\u003EPrompt Engineering with GPT-3.5 model\u003C/h3\u003E\n","\u003Cp\u003ELet us start with defining the \u003Ccode\u003EPROD_DESC_CHATGPT35_V1\u003C/code\u003E function:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE FUNCTION PROD_DESC_CHATGPT35_V1(query varchar)\nRETURNS STRING\nLANGUAGE PYTHON\nRUNTIME_VERSION = 3.9\nHANDLER = 'complete_me'\nEXTERNAL_ACCESS_INTEGRATIONS = (vino_external_access_int)\nSECRETS = ('openai_key' = vino_open_ai_api)\nPACKAGES = ('openai')\nAS\n$$\nimport _snowflake\nimport openai\nopenai.api_key = _snowflake.get_generic_secret_string('openai_key')\nmodel="gpt-3.5-turbo"\nprompt="Explain in details what the ingredients and capabilities of this cosmetic product are. Limit the response to 150 words only. Here is the product:"\ndef complete_me(QUERY):\n    messages=[\n    {'role': 'user', 'content':f"{prompt} {QUERY}"}\n    ]\n    response = openai.ChatCompletion.create(model=model,messages=messages,temperature=0)    \n    return response.choices[0].message["content"]\n$$;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ELet's call this function with a sample query:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT PROD_DESC_CHATGPT35_V1('Magnesium Lotion With Aloe Vera, Shea Butter, Coconut Oil & Magnesium Oil For Muscle Pain & Leg Cramps – Rich In Magnesium Chloride And Vitamin E Oil') as response;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cem\u003E\u003Cstrong\u003ESample response:\u003C/strong\u003E\u003C/em\u003E \u003Cem\u003E"This cosmetic product, the Magnesium Lotion with Aloe Vera, Shea Butter, Coconut Oil, and Magnesium Oil, is specifically designed to alleviate muscle pain and leg cramps. It contains a combination of key ingredients that work synergistically to provide relief and nourishment to the skin. The lotion is rich in Magnesium Chloride, a mineral known for its muscle relaxing properties. Magnesium Chloride helps to reduce muscle tension, ease cramps, and promote relaxation. Additionally, it aids in the absorption of magnesium into the body, which is essential for maintaining healthy muscle function. Aloe Vera is included in the formula for its soothing and moisturizing properties. It helps to calm irritated skin, reduce inflammation, and provide hydration. Shea Butter and Coconut Oil are both deeply moisturizing ingredients that help to nourish and soften the skin. They create a protective barrier that locks in moisture, leaving the skin feeling smooth and supple. Vitamin E Oil is added for its antioxidant properties, which help to protect the skin from free radicals and environmental damage. Overall, this Magnesium Lotion combines the benefits of Magnesium Chloride, Aloe Vera, Shea Butter, Coconut Oil, and Vitamin E Oil to provide relief from muscle pain and leg cramps while simultaneously nourishing and hydrating the skin."\u003C/em\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT PROD_DESC_CHATGPT35_V1('Tandoori Mixed Grill') as response;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cem\u003E\u003Cstrong\u003ESample response:\u003C/strong\u003E\u003C/em\u003E \u003Cem\u003E"Tandoori Mixed Grill is not a cosmetic product, but rather a popular Indian dish consisting of a variety of marinated meats and vegetables cooked in a traditional clay oven called a tandoor. It typically includes ingredients such as chicken, lamb, fish, or shrimp, along with a blend of spices and yogurt-based marinade. The marinade is made using a combination of ingredients like ginger, garlic, turmeric, cumin, coriander, paprika, garam masala, and yogurt, which help to tenderize the meat and infuse it with rich flavors. The capabilities of Tandoori Mixed Grill lie in its ability to provide a delicious and aromatic culinary experience. The combination of spices and yogurt-based marinade not only enhances the taste of the meats and vegetables but also helps to tenderize and moisten them during the cooking process. The high heat of the tandoor oven imparts a smoky and charred flavor to the dish, giving it a unique and authentic taste. Tandoori Mixed Grill is often served with naan bread, rice, or a side salad, making it a satisfying and flavorful meal option."\u003C/em\u003E\u003C/p\u003E\n","\u003Cp\u003EOur dataset contains only cosmetic products. So we want the LLM to generate descriptions for cosmetic products only. So let us tweak our prompt to achieve this.\u003C/p\u003E\n","\u003Cp\u003EAfter tweaking the prompt, let's create a new function \u003Ccode\u003EPROD_DESC_CHATGPT35_V2\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE FUNCTION PROD_DESC_CHATGPT35_V2(query varchar)\nRETURNS STRING\nLANGUAGE PYTHON\nRUNTIME_VERSION = 3.9\nHANDLER = 'complete_me'\nEXTERNAL_ACCESS_INTEGRATIONS = (vino_external_access_int)\nSECRETS = ('openai_key' = vino_open_ai_api)\nPACKAGES = ('openai')\nAS\n$$\nimport _snowflake\nimport openai\n\nopenai.api_key = _snowflake.get_generic_secret_string('openai_key')\nmodel="gpt-3.5-turbo"\nprompt="Explain in detail what the ingredients and capabilities of this cosmetic product are. Limit the response to 150 words only. Respond with OOPS!! it is not a beauty product if the product in question is not a cosmetic or a beauty product. Here is the product:"\n\ndef complete_me(QUERY):\n    messages=[\n    {'role': 'user', 'content':f"{prompt} {QUERY}"}\n    ]\n    response = openai.ChatCompletion.create(model=model,messages=messages,temperature=0)    \n    return response.choices[0].message["content"]\n$$;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ELet's call this function with the same sample queries again:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT PROD_DESC_CHATGPT35_V2('Magnesium Lotion With Aloe Vera, Shea Butter, Coconut Oil & Magnesium Oil For Muscle Pain & Leg Cramps – Rich In Magnesium Chloride And Vitamin E Oil') as response;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cem\u003E\u003Cstrong\u003ESample response:\u003C/strong\u003E\u003C/em\u003E *"The Magnesium Lotion with Aloe Vera, Shea Butter, Coconut Oil, and Magnesium Oil is a beauty product designed to alleviate muscle pain and leg cramps. It contains a combination of ingredients that work synergistically to provide relief and nourishment to the skin.\u003C/p\u003E\n","\u003Cp\u003EMagnesium chloride is a key ingredient in this lotion, known for its ability to relax muscles and reduce muscle pain. It is easily absorbed through the skin, making it an effective topical treatment. Aloe Vera is included for its soothing and moisturizing properties, helping to calm irritated skin and promote healing. Shea butter and coconut oil are both rich in essential fatty acids and vitamins, providing deep hydration and nourishment to the skin. These ingredients help to improve skin elasticity and promote a healthy, youthful appearance. Vitamin E oil is added for its antioxidant properties, protecting the skin from free radicals and environmental damage. It also aids in reducing inflammation and promoting skin healing. Overall, this Magnesium Lotion combines the benefits of magnesium chloride, aloe vera, shea butter, coconut oil, and vitamin E oil to provide relief from muscle pain and leg cramps while nourishing and rejuvenating the skin."*\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT PROD_DESC_CHATGPT35_V2('Tandoori Mixed Grill') as response;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cem\u003E\u003Cstrong\u003ESample response:\u003C/strong\u003E\u003C/em\u003E \u003Cem\u003E"OOPS!! it is not a beauty product. The Tandoori Mixed Grill is not a cosmetic or beauty product. It is a popular Indian dish that consists of a variety of marinated and grilled meats and vegetables. The ingredients typically include chicken, lamb, and/or shrimp, along with a mixture of spices and yogurt-based marinade.The capabilities of the Tandoori Mixed Grill are centered around its delicious taste and flavor. The meats and vegetables are marinated in a blend of spices, such as cumin, coriander, turmeric, paprika, and garam masala, which gives them a rich and aromatic profile. The yogurt-based marinade helps to tenderize the meats and adds a creamy texture to the dish. When cooked in a tandoor oven or on a grill, the Tandoori Mixed Grill develops a smoky and charred flavor, while still retaining its juicy and tender texture. It is often served with naan bread, rice, and a side of mint chutney or raita. Overall, the Tandoori Mixed Grill is a flavorful and satisfying dish that showcases the vibrant and aromatic flavors of Indian cuisine."\u003C/em\u003E\u003C/p\u003E\n","\u003Cp\u003EInteresting! For non-cosmetic products, the response is not right. Now let's generate product descriptions for all the products in our products table.\u003C/p\u003E\n","\u003Cp\u003EWe have played around with only 2 versions of the prompts. You can iterate on the prompts as many times as you want until the model produces desired output.\u003C/p\u003E\n","\u003Ch3\u003EPrompt Engineering with GPT-4 model\u003C/h3\u003E\n","\u003Cp\u003ENow, let us repeat the experiment but using GPT-4 model instead.\u003C/p\u003E\n","\u003Cp\u003ELet us start with defining the \u003Ccode\u003EPROD_DESC_CHATGPT4_V1\u003C/code\u003E function:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE FUNCTION PROD_DESC_CHATGPT4_V1(query varchar)\nRETURNS STRING\nLANGUAGE PYTHON\nRUNTIME_VERSION = 3.9\nHANDLER = 'complete_me'\nEXTERNAL_ACCESS_INTEGRATIONS = (vino_external_access_int)\nSECRETS = ('openai_key' = vino_open_ai_api)\nPACKAGES = ('openai')\nAS\n$$\nimport _snowflake\nimport openai\ndef complete_me(QUERY):\n    openai.api_key = _snowflake.get_generic_secret_string('openai_key')\n    messages = [{"role": "system", "content": "Explain in details what the ingredients and capabilities of this cosmetic product are. Limit the response to 150 words only. Here is the product: "}, {"role": "user", "content": QUERY}]\n    model="gpt-4"\n    response = openai.ChatCompletion.create(model=model,messages=messages,temperature=0,)    \n    return response.choices[0].message["content"]\n$$;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ELet's call this function with the same sample queries again:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT PROD_DESC_CHATGPT4_V1('Magnesium Lotion With Aloe Vera, Shea Butter, Coconut Oil & Magnesium Oil For Muscle Pain & Leg Cramps – Rich In Magnesium Chloride And Vitamin E Oil') as response;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cem\u003E\u003Cstrong\u003ESample response:\u003C/strong\u003E\u003C/em\u003E \u003Cem\u003E"This cosmetic product is a magnesium lotion enriched with Aloe Vera, Shea Butter, Coconut Oil, Magnesium Oil, Magnesium Chloride, and Vitamin E Oil. Magnesium Oil and Magnesium Chloride are known for their muscle relaxation properties, helping to alleviate muscle pain and leg cramps. Aloe Vera is renowned for its soothing and moisturizing capabilities, making the skin soft and supple. Shea Butter is a natural emollient that nourishes the skin deeply, while Coconut Oil provides hydration and prevents dryness. Vitamin E Oil is a powerful antioxidant that protects the skin from environmental damage and promotes healing. Together, these ingredients work synergistically to provide relief from muscle discomfort while enhancing the skin's overall health and appearance."\u003C/em\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT PROD_DESC_CHATGPT4_V1('Tandoori Mixed Grill') as response;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cem\u003E\u003Cstrong\u003ESample response:\u003C/strong\u003E\u003C/em\u003E \u003Cem\u003E"It seems there's been a misunderstanding. Tandoori Mixed Grill is not a cosmetic product. It's a popular Indian dish that includes a variety of marinated meats like chicken, lamb, and shrimp, typically cooked in a tandoor, a traditional clay oven. The marinade usually contains yogurt and spices like turmeric, cumin, and coriander. However, if you have a cosmetic product you'd like information on, feel free to provide its details."\u003C/em\u003E\u003C/p\u003E\n","\u003Cp\u003EThe responses look appropriate. How can we tune the prompts even better?\u003C/p\u003E\n","\u003Cp\u003ELet's run the experiment again with an updated prompt, and define the \u003Ccode\u003EPROD_DESC_CHATGPT4_V2\u003C/code\u003E function:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE FUNCTION PROD_DESC_CHATGPT4_V2(query varchar)\nRETURNS STRING\nLANGUAGE PYTHON\nRUNTIME_VERSION = 3.9\nHANDLER = 'complete_me'\nEXTERNAL_ACCESS_INTEGRATIONS = (vino_external_access_int)\nSECRETS = ('openai_key' = vino_open_ai_api)\nPACKAGES = ('openai')\nAS\n$$\nimport _snowflake\nimport openai\ndef complete_me(QUERY):\n    openai.api_key = _snowflake.get_generic_secret_string('openai_key')\n    messages = [{"role": "system", "content": "You are an AI assistant who only knows about different cosmetic products and can explain in detail what the ingredients and capabilities of the product are. Limit the response to 150 words only. Here is the product: "}, {"role": "user", "content": QUERY}]\n    model="gpt-4"\n    response = openai.ChatCompletion.create(model=model,messages=messages,temperature=0,)    \n    return response.choices[0].message["content"]\n$$;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ELet's call this function with the same sample queries again:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT PROD_DESC_CHATGPT4_V2('Magnesium Lotion With Aloe Vera, Shea Butter, Coconut Oil & Magnesium Oil For Muscle Pain & Leg Cramps – Rich In Magnesium Chloride And Vitamin E Oil') as response;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cem\u003E\u003Cstrong\u003ESample response:\u003C/strong\u003E\u003C/em\u003E \u003Cem\u003E"Magnesium Lotion with Aloe Vera, Shea Butter, Coconut Oil & Magnesium Oil is a multi-functional cosmetic product. Magnesium Chloride, its key ingredient, is known for relieving muscle pain and leg cramps. Aloe Vera, a natural moisturizer, soothes and hydrates the skin. Shea Butter, rich in vitamins A and E, provides deep nourishment and promotes skin elasticity. Coconut Oil, packed with fatty acids, offers intense hydration and has anti-inflammatory properties. Magnesium Oil aids in muscle relaxation and helps improve skin health. Vitamin E Oil, a powerful antioxidant, protects the skin from damage and promotes healing. This lotion is a blend of natural ingredients designed to provide relief from muscle discomfort while enhancing skin health and vitality."\u003C/em\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT PROD_DESC_CHATGPT4_V2('Tandoori Mixed Grill') as response;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cem\u003E\u003Cstrong\u003ESample response:\u003C/strong\u003E\u003C/em\u003E \u003Cem\u003E"I'm sorry for the confusion, but as an AI assistant specializing in cosmetic products, I can't provide detailed information about food items like Tandoori Mixed Grill. My expertise lies in explaining the ingredients and capabilities of cosmetic products such as skincare, makeup, haircare, and more. If you have any questions about a specific cosmetic product, feel free to ask!"\u003C/em\u003E\u003C/p\u003E\n","\u003Ch3\u003EComparing GPT-4 model responses from prompt engineering\u003C/h3\u003E\n","\u003Cp\u003EAfter we experiment with different variation of prompts, we need a human in the loop to evaluate the LLM responses for each prompt.\u003C/p\u003E\n","\u003Cp\u003ESo let's save the product names and the product descriptions generated by different model prompts in a Snowflake table. Copy, Paste and run the following query in your SQL worksheet to create the \u003Ccode\u003EPRODUCT_DESC_GPT4\u003C/code\u003E table.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE TABLE PRODUCT_DESC_GPT4\nAS\nSELECT product_name, \n    PROD_DESC_CHATGPT4_V1(product_name) as DESC_V1,\n    PROD_DESC_CHATGPT4_V2(product_name) as DESC_V2\nFROM PRODUCT;\n\nSELECT * \nFROM PRODUCT_DESC_GPT4;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Chr\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/prompt-engineering-and-llm-evaluation/product_desc.png\" alt=\"Product Description\"\u003E\u003C/p\u003E\n\u003Chr\u003E\n","\u003Cp\u003ELet us also add a \u003Ccode\u003Ereview\u003C/code\u003E column to the table to store the human reviews of model responses.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER TABLE PRODUCT_DESC_GPT4 ADD REVIEW STRING(2);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFun part is you can work with Snowflake data and OpenAI models together using Snowflake External Access functionality.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ELLM Evaluation with Human Feedback\u003C/h2\u003E\n","\u003Cp\u003EFollow these steps to build a Streamlit application in Snowsight.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 1.\u003C/strong\u003E Click on \u003Cstrong\u003EStreamlit\u003C/strong\u003E on the left navigation menu\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 2.\u003C/strong\u003E Click on \u003Cstrong\u003E+ Streamlit App\u003C/strong\u003E on the top right\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 3.\u003C/strong\u003E Enter \u003Cstrong\u003EApp name\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 4.\u003C/strong\u003E Select \u003Cstrong\u003EWarehouse\u003C/strong\u003E (VINO_CUSTOMER_EXP_WH_M) and \u003Cstrong\u003EApp location\u003C/strong\u003E (Database as CUSTOMER_EXP_DB and Schema as CUSTOMER_EXP_SCHEMA)\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 5.\u003C/strong\u003E Click on \u003Cstrong\u003ECreate\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EAt this point, you will be provided code for an example Streamlit application\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 6.\u003C/strong\u003E Replace sample application code displayed in the code editor on the left with the code provided in \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-prompt-engineering-and-llm-evaluation/blob/main/llm_evaluation_sis_app.py\"\u003Ellm_evaluation_sis_app.py\u003C/a\u003E)\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 7.\u003C/strong\u003E Click on \u003Cstrong\u003ERun\u003C/strong\u003E on the top right\u003C/p\u003E\n","\u003Cp\u003EIf all goes well, you should see the application in Snowsight as shown below.\u003C/p\u003E\n\u003Chr\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/prompt-engineering-and-llm-evaluation/app_sis.png\" alt=\"Streamlit-in-Snowflake\"\u003E\u003C/p\u003E\n\u003Chr\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 8.\u003C/strong\u003E In the Streamlit application, you can see the product name along with two different model responses. You can click on the description to read the entire text and write your feedback in the \u003Ccode\u003Ereview\u003C/code\u003E column.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 9.\u003C/strong\u003E Once you are done reviewing the responses, you could click on \u003Ccode\u003EUpdate Reviews\u003C/code\u003E button to save the reviews in the \u003Ccode\u003EPRODUCT_DESC_GPT4\u003C/code\u003E table.\u003C/p\u003E\n","\u003Cp\u003EVoila. It's done!\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations! You have successfully learnt the basics of prompt engineering on your Large Language Models (LLMs) and to evaluate the responses of different LLMs through human feedback by building an interative Streamlit Application.\u003C/p\u003E\n","\u003Ch3\u003EWhat you learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to access data from Snowflake Marketplace and use it for your analysis\u003C/li\u003E\u003Cli\u003EHow to use External Access to securely connect to the OpenAI API from Snowpark\u003C/li\u003E\u003Cli\u003EHow to use different prompts on a Large Language Model and capture model responses in a Snowflake table\u003C/li\u003E\u003Cli\u003EHow to build a Streamlit App to compare the model responses and capture the review in a Snowflake table\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n","\u003Cp\u003EWant to learn more about the tools and technologies used by your app? Check out the following resources:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-prompt-engineering-and-llm-evaluation\"\u003ESource Code on GitHub\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/external-network-access/external-network-access-overview\"\u003EExternal Access in Snowflake\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://platform.openai.com/docs/api-reference/completions\"\u003EOpenAI's Completions API\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://developers.snowflake.com/demos/generative-ai-llm-snowflake/\"\u003EDemo on Snowflake Demo Hub\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://streamlit.io/generative-ai\"\u003EBuild powerful generative AI apps with Streamlit\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Prompt Engineering and Evaluation of LLM responses","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"value":"\u003C!-- ------------------------ --\u003E\r\n## Overview \r\n\r\n\r\nThis quickstart will cover the basics of prompt engineering on your Large Language Models (LLMs) and how to evaluate the responses of different LLMs through human feedback.\r\n\r\nBy completing this guide, you will learn how to run AI experimentation with different LLMs for your use case. First part of the quickstart focusses on using prompt engineering to generate different model responses by tweaking your prompts. The second part focusses on evaluating different LLM model responses through human feedback.\r\n\r\nHere is a summary of what you will be able to learn in each step following this quickstart:\r\n\r\n- **Setup Environment**: Setup your Snowflake Free Trial environment\r\n- **Snowflake Marketplace**: Download the data you need from Snowflake Marketplace and use it in your analysis\r\n- **Snowflake External Access**: Integrate LLMs such as GPT-3.5 and GPT4 to Snowflake using External Access\r\n- **Prompt Engineering**: Use different prompts on an LLM and capture the responses into a Snowflake table\r\n- **LLM Evaluation with Human Feedback**: Build a Streamlit App to compare and rate the LLM responses and capture the ratings in a Snowflake table\r\n- **What's next?**: What other applications can you build for your industry and data, inspired from this quickstart?\r\n\r\nLet's dive into the key features and technologies used in the demo, for better understanding.\r\n\r\n### Key features & technology\r\n\r\n- Large language models (LLMs)\r\n- Prompt Engineering\r\n- LLM Evaluation\r\n- Snowflake External Access\r\n- Snowflake Marketplace\r\n- Streamlit\r\n\r\n### What is a large language model (LLM)?\r\n\r\nA large language model, or LLM, is a deep learning algorithm that can recognize, summarize, translate, predict and generate text and other content based on knowledge gained from massive datasets. Some examples of popular LLMs are [GPT-4](https://openai.com/research/gpt-4), [GPT-3](https://openai.com/blog/gpt-3-apps), [BERT](https://cloud.google.com/ai-platform/training/docs/algorithms/bert-start), [LLaMA](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/), and [LaMDA](https://blog.google/technology/ai/lamda/).\r\n\r\n### What is OpenAI?\r\n\r\nOpenAI is the AI research and deployment company behind ChatGPT, GPT-4 (and its predecessors), DALL-E, and other notable offerings. Learn more about [OpenAI](https://openai.com/). We use OpenAI in this guide, but you are welcome to use the large language model of your choice in its place.\r\n\r\n### What is Prompt Engineering?\r\n\r\nA prompt is a natural language text that requests the Generative AI model to perform a specific task. Prompt Engineering is the process of guiding the AI model to generate a specific response by tweaking the prompt text. Prompt Engineering uses a mix of creativity and experimentation to identify the right set of prompts that ensures the AI model will return the desired response.\r\n\r\n### What is LLM Evaluation?\r\n\r\nSince Large Language Model outputs free form texts that may not have the ground truth, it can be difficult to evaluate the LLMs using traditional model metrics such as Accuracy, Precision, Recall, F1-Score, etc. There are multiple frameworks in development to evaluate the bias, toxicity and hallucination in LLM responses. However, the most popular and efficient method has been human-in-the-loop evaluation. That is, letting humans rate the model responses as helpful and right.\r\n\r\n### What is Snowflake External Access?\r\n\r\n[External Access](https://docs.snowflake.com/en/developer-guide/external-network-access/external-network-access-overview) helps to securely connect to the OpenAI API from [Snowpark](https://docs.snowflake.com/en/developer-guide/snowpark/index), the set of libraries and runtimes in Snowflake that run non-SQL code, including Python, Java and Scala. External Access provides flexibility to reach public internet endpoints from the Snowpark sandbox without any additional infrastructure setup.\r\n\r\n### What is the Snowflake Marketplace?\r\n\r\nThe [Snowflake Marketplace](/en/data-cloud/marketplace/) provides users with access to a wide range of datasets from third-party data stewards, expanding the data available for transforming business processes and making decisions. Data providers can publish datasets and offer data analytics services to Snowflake customers. Customers can securely access shared datasets directly from their Snowflake accounts and receive automatic real-time updates.\r\n\r\n### What is Streamlit?\r\n\r\nStreamlit enables data scientists and Python developers to combine Streamlit's component-rich, open-source Python library with the scale, performance, and security of the Snowflake platform. Learn more about [Streamlit](https://streamlit.io/).\r\n\r\n### What You Will Learn\r\n\r\n- How to access data from Snowflake Marketplace and use it for your analysis\r\n- How to use External Access to securely connect to the OpenAI API from Snowpark\r\n- How to use different prompts on a Large Language Model and capture model responses in a Snowflake table\r\n- How to build a Streamlit App to compare the model responses and capture the review in a Snowflake table\r\n\r\n### Prerequisites\r\n\r\n- A **Snowflake account** with ACCOUNTADMIN role access - [Sign-in or create a free trial account](https://signup.snowflake.com/?utm_cta=quickstarts_)\r\n- **Anaconda Terms & Conditions accepted**. See Getting Started section in [Third-Party Packages](https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-packages.html#getting-started).\r\n- **GitHub account** - If you don't already have a GitHub account you can create one for free. Visit the [Join GitHub](https://github.com/signup) page to get started.\r\n- A **OpenAI account** or API key to another language model - [Sign-in or create an account](https://openai.com/)\r\n  - [OpenAI API Key](https://platform.openai.com/account/api-keys)\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n\r\n## Set up Snowflake\r\n\r\n\r\nSign up for [Snowflake Free Trial](https://signup.snowflake.com/?utm_cta=quickstarts_) and create an account. Log into [Snowsight](https://docs.snowflake.com/en/user-guide/ui-snowsight.html#) using your credentials to create tables, streamlit app and more.\r\n\r\n\u003E aside positive\r\n\u003E IMPORTANT:\r\n\u003E\r\n\u003E - If you use different names for objects created in this section, be sure to update scripts and code in the following sections accordingly.\r\n\u003E\r\n\u003E - For each SQL script block below, select all the statements in the block and execute them top to bottom.\r\n\r\n### Clone GitHub Repository\r\n\r\nThe very first step is to clone the [GitHub repository](https://github.com/Snowflake-Labs/sfguide-prompt-engineering-and-llm-evaluation). This repository contains all the code you will need to successfully complete this QuickStart Guide.\r\n\r\nUsing HTTPS:\r\n\r\n```shell\r\ngit clone https://github.com/Snowflake-Labs/sfguide-prompt-engineering-and-llm-evaluation.git\r\n```\r\n\r\nOR, using SSH:\r\n\r\n```shell\r\ngit clone git@github.com:Snowflake-Labs/sfguide-prompt-engineering-and-llm-evaluation.git\r\n```\r\n\r\n### Create Databases, Tables and Warehouses\r\n\r\nRun the following SQL commands to create the [warehouse](https://docs.snowflake.com/en/sql-reference/sql/create-warehouse.html), [database](https://docs.snowflake.com/en/sql-reference/sql/create-database.html) and [schema](https://docs.snowflake.com/en/sql-reference/sql/create-schema.html).\r\n\r\n```sql\r\nCREATE OR REPLACE DATABASE CUSTOMER_EXP_DB;\r\nCREATE OR REPLACE SCHEMA CUSTOMER_EXP_SCHEMA;\r\nCREATE OR REPLACE WAREHOUSE VINO_CUSTOMER_EXP_WH_M WAREHOUSE_SIZE='MEDIUM';\r\n```\r\n\r\nRun the following commands to change the scope to the database, schema and warehouse created above.\r\n\r\n```sql\r\nUSE DATABASE CUSTOMER_EXP_DB;\r\nUSE SCHEMA CUSTOMER_EXP_SCHEMA;\r\nUSE WAREHOUSE VINO_CUSTOMER_EXP_WH_M;\r\n```\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n\r\n## Snowflake Marketplace\r\n\r\nDuring this step, we will be loading customer experience data to Snowflake. But \"loading\" is really the wrong word here. Because we're using Snowflake's unique data sharing capability we don't actually need to copy the data to our Snowflake account with a custom ETL process. Instead we can directly access the customer review data shared by `Clootrack` in the Snowflake Marketplace.\r\n\r\n### Customer Experience data from Snowflake Marketplace\r\n\r\nLet's connect to the data from Snowflake Marketplace by following these steps:\r\n\r\n- Log into Snowsight\r\n- Click on the `Marketplace` tab in the left navigation bar\r\n- Enter `Customer Experience Score by Product` in the search box and click return\r\n- Click on the `Sample - USA` listing title\r\n- Click the blue `Get` button\r\n  - Expand the `Options` dialog\r\n  - Change the database name to read `CUSTOMER_EXP` (all capital letters)\r\n  - Select the `ACCOUNTADMIN` role to have access to the new database\r\n  - Click the blue `Get` button\r\n\r\n---\r\n\r\n![Snowflake Marketplace](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/prompt-engineering-and-llm-evaluation/marketplace.png)\r\n\r\n---\r\n\r\nThat's it... we have the dataset to work with.\r\n\r\n### Query the Marketplace data\r\n\r\nRun the following queries to peek into the data we got from the Marketplace.\r\n\r\n```sql\r\nSELECT COUNT(*) as row_count \r\nFROM CUSTOMER_EXP.PUBLIC.TRIAL_PRODUCT_CUSTOMER_EXPERIENCE_VIEW;\r\n\r\nSELECT * \r\nFROM CUSTOMER_EXP.PUBLIC.TRIAL_PRODUCT_CUSTOMER_EXPERIENCE_VIEW\r\nLIMIT 5;\r\n```\r\n\r\nYou can also view the shared database `CUSTOMER_EXP.PUBLIC.TRIAL_PRODUCT_CUSTOMER_EXPERIENCE_VIEW` by navigating to the Snowsight UI -\u003E Data -\u003E Databases.\r\n\r\n### Copy the Marketplace data into our Database\r\n\r\nThe database and the view we got from the Marketplace are read-only. In order to be able to write data into the table, we need to copy `CUSTOMER_EXP.PUBLIC.TRIAL_PRODUCT_CUSTOMER_EXPERIENCE_VIEW` view into another table.\r\n\r\nRun the following SQL command to create a new table and copy data into the new table\r\n\r\n```sql\r\nCREATE OR REPLACE TABLE CUSTOMER_EXP_REVIEWS (\r\n    brand_name VARCHAR(100),\r\n    product_name VARCHAR(1000),\r\n    sub_category VARCHAR(100),\r\n    positive_customer_exp NUMBER(10,2),\r\n    sentence_count NUMBER(10),\r\n    month STRING(15),\r\n    year NUMBER(4),\r\n    start_date DATE,\r\n    end_date DATE\r\n);\r\n\r\nINSERT INTO CUSTOMER_EXP_REVIEWS\r\nSELECT * \r\nFROM CUSTOMER_EXP.PUBLIC.TRIAL_PRODUCT_CUSTOMER_EXPERIENCE_VIEW;\r\n```\r\n\r\nLet us quickly review the data from the newly created table\r\n\r\n```sql\r\nSELECT COUNT(*) \r\nFROM CUSTOMER_EXP_REVIEWS;\r\n\r\nSELECT *\r\nFROM CUSTOMER_EXP_REVIEWS\r\nLIMIT 5;\r\n```\r\n\r\nThe table has several columns but the `product_name` column will be of interest to us. What if we used the LLMs to create a detailed product description from the product_name column? Let's do it.\r\n\r\nLet us create a new table called `PRODUCT` to store the unique product names from the CUSTOMER_EXP_REVIEWS table.\r\n\r\n```sql\r\nCREATE OR REPLACE TABLE PRODUCT \r\nAS\r\nSELECT DISTINCT product_name\r\nFROM CUSTOMER_EXP_REVIEWS\r\nLIMIT 10;\r\n\r\nSELECT COUNT(*)\r\nFROM PRODUCT;\r\n\r\nSELECT * \r\nFROM PRODUCT;\r\n```\r\n\r\n\u003E aside positive\r\n\u003E IMPORTANT:\r\n\u003E\r\n\u003E - If you used a different name for the database while getting weather data from the marketplace, be sure to update scripts and code in the following sections accordingly.\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n\r\n## Use External Access\r\n\r\n\r\nIn this implementation, we will use External Access to securely connect to the OpenAI API from Snowpark Python.\r\n\r\n### Step 1\r\n\r\nCreate a [Secret](https://docs.snowflake.com/en/sql-reference/sql/create-secret) object in Snowflake to securely store your OpenAI API key.\r\n\r\n```sql\r\nCREATE OR REPLACE SECRET vino_open_ai_api\r\n TYPE = GENERIC_STRING\r\n SECRET_STRING = 'YOUR-OPENAI-API-KEY';\r\n```\r\n\r\n### Step 2\r\n\r\nCreate a [Network Rule](https://docs.snowflake.com/en/sql-reference/sql/create-network-rule) object in Snowflake.\r\n\r\n```sql\r\nCREATE OR REPLACE NETWORK RULE vino_apis_network_rule\r\n MODE = EGRESS\r\n TYPE = HOST_PORT\r\n VALUE_LIST = ('api.openai.com');\r\n```\r\n\r\n### Step 3\r\n\r\nCreate a [External Access Integration](https://docs.snowflake.com/en/sql-reference/sql/create-external-access-integration) object in Snowflake.\r\n\r\n```sql\r\nCREATE OR REPLACE EXTERNAL ACCESS INTEGRATION vino_external_access_int\r\n ALLOWED_NETWORK_RULES = (vino_apis_network_rule)\r\n ALLOWED_AUTHENTICATION_SECRETS = (vino_open_ai_api)\r\n ENABLED = true;\r\n```\r\n\r\n### Step 4\r\n\r\nCreate Snowpark Python function in Snowflake. Let us understand what this SQL query does:\r\n\r\n- It uses Python 3.9 runtime\r\n- It uses the package `openai`\r\n- It uses the external access integration we created in the above step\r\n- `complete_me()` function takes the query we pass, the prompt we have defined and invokes ChatCompletion API of OpenAI's `gpt-3.5-turbo` model\r\n- It returns the model response in the end\r\n\r\n```sql\r\nCREATE OR REPLACE FUNCTION PROD_DESC_CHATGPT35_V1(query varchar)\r\nRETURNS STRING\r\nLANGUAGE PYTHON\r\nRUNTIME_VERSION = 3.9\r\nHANDLER = 'complete_me'\r\nEXTERNAL_ACCESS_INTEGRATIONS = (vino_external_access_int)\r\nSECRETS = ('openai_key' = vino_open_ai_api)\r\nPACKAGES = ('openai')\r\nAS\r\n$$\r\nimport _snowflake\r\nimport openai\r\nopenai.api_key = _snowflake.get_generic_secret_string('openai_key')\r\nmodel=\"gpt-3.5-turbo\"\r\nprompt=\"Explain in details what the ingredients and capabilities of this cosmetic product are. Limit the response to 150 words only. Here is the product:\"\r\ndef complete_me(QUERY):\r\n    messages=[\r\n    {'role': 'user', 'content':f\"{prompt} {QUERY}\"}\r\n    ]\r\n    response = openai.ChatCompletion.create(model=model,messages=messages,temperature=0)    \r\n    return response.choices[0].message[\"content\"]\r\n$$;\r\n```\r\n\r\n### **Call Function in Snowflake**\r\n\r\nAssuming you have completed all the prerequites, calling the function is as simple as follow:\r\n\r\n```sql\r\nSELECT PROD_DESC_CHATGPT35_V1('Magnesium Lotion With Aloe Vera, Shea Butter, Coconut Oil & Magnesium Oil For Muscle Pain & Leg Cramps – Rich In Magnesium Chloride And Vitamin E Oil') as response;\r\n```\r\n\r\n***Sample response:*** *\"This cosmetic product, the Magnesium Lotion with Aloe Vera, Shea Butter, Coconut Oil, and Magnesium Oil, is specifically designed to alleviate muscle pain and leg cramps. It contains a combination of key ingredients that work synergistically to provide relief and nourishment to the skin.The lotion is rich in Magnesium Chloride, a mineral known for its muscle relaxing properties. Magnesium Chloride helps to reduce muscle tension, ease cramps, and promote relaxation. Additionally, it aids in the absorption of magnesium into the body, which is essential for maintaining healthy muscle function.Aloe Vera is included in the formula for its soothing and moisturizing properties. It helps to calm irritated skin, reduce inflammation, and provide hydration.Shea Butter and Coconut Oil are both deeply moisturizing ingredients that help to nourish and soften the skin. They create a protective barrier that locks in moisture, leaving the skin feeling smooth and supple.Vitamin E Oil is added for its antioxidant properties, which help to protect the skin from free radicals and environmental damage.Overall, this Magnesium Lotion combines the benefits of Magnesium Chloride, Aloe Vera, Shea Butter, Coconut Oil, and Vitamin E Oil to provide relief from muscle pain and leg cramps while simultaneously nourishing and hydrating the skin.\"*\r\n\r\n```sql\r\nSELECT PROD_DESC_CHATGPT35_V1('Tandoori Mixed Grill') as response;\r\n```\r\n\r\n***Sample response:*** *\"Tandoori Mixed Grill is not a cosmetic product, but rather a popular Indian dish consisting of a variety of marinated meats and vegetables cooked in a traditional clay oven called a tandoor. It typically includes ingredients such as chicken, lamb, fish, or shrimp, along with a blend of spices and yogurt-based marinade. The marinade is made using a combination of ingredients like ginger, garlic, turmeric, cumin, coriander, paprika, garam masala, and yogurt, which help to tenderize the meat and infuse it with rich flavors. The capabilities of Tandoori Mixed Grill lie in its ability to provide a delicious and aromatic culinary experience. The combination of spices and yogurt-based marinade not only enhances the taste of the meats and vegetables but also helps to tenderize and moisten them during the cooking process. The high heat of the tandoor oven imparts a smoky and charred flavor to the dish, giving it a unique and authentic taste. Tandoori Mixed Grill is often served with naan bread, rice, or a side salad, making it a satisfying and flavorful meal option.\"*\r\n\r\nVery cool. Isn't it? Now we are able to call gpt-3.5-turbo model APIs from Snowflake. You can also use `GPT` model on your Snowflake data.\r\n\r\nNow that we are able to access OpenAI's gpt model from Snowflake, let's go ahead and try out the Snowpark `PROD_DESC_CHATGPT35_V1` function on our Snowflake data.\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Prompt Engineering\r\n\r\nSo far, we created a snowpark function to invoke the OpenAI's ChatCompletion APIs. We got access to the Customer Experience data from Marketplace. In this step, let us use the OpenAI APIs to generate detailed product descriptions for the products in our table.\r\n\r\n### Prompt Engineering with GPT-3.5 model\r\n\r\nLet us start with defining the `PROD_DESC_CHATGPT35_V1` function:\r\n\r\n```sql\r\nCREATE OR REPLACE FUNCTION PROD_DESC_CHATGPT35_V1(query varchar)\r\nRETURNS STRING\r\nLANGUAGE PYTHON\r\nRUNTIME_VERSION = 3.9\r\nHANDLER = 'complete_me'\r\nEXTERNAL_ACCESS_INTEGRATIONS = (vino_external_access_int)\r\nSECRETS = ('openai_key' = vino_open_ai_api)\r\nPACKAGES = ('openai')\r\nAS\r\n$$\r\nimport _snowflake\r\nimport openai\r\nopenai.api_key = _snowflake.get_generic_secret_string('openai_key')\r\nmodel=\"gpt-3.5-turbo\"\r\nprompt=\"Explain in details what the ingredients and capabilities of this cosmetic product are. Limit the response to 150 words only. Here is the product:\"\r\ndef complete_me(QUERY):\r\n    messages=[\r\n    {'role': 'user', 'content':f\"{prompt} {QUERY}\"}\r\n    ]\r\n    response = openai.ChatCompletion.create(model=model,messages=messages,temperature=0)    \r\n    return response.choices[0].message[\"content\"]\r\n$$;\r\n```\r\n\r\nLet's call this function with a sample query:\r\n\r\n```sql\r\nSELECT PROD_DESC_CHATGPT35_V1('Magnesium Lotion With Aloe Vera, Shea Butter, Coconut Oil & Magnesium Oil For Muscle Pain & Leg Cramps – Rich In Magnesium Chloride And Vitamin E Oil') as response;\r\n```\r\n\r\n***Sample response:*** *\"This cosmetic product, the Magnesium Lotion with Aloe Vera, Shea Butter, Coconut Oil, and Magnesium Oil, is specifically designed to alleviate muscle pain and leg cramps. It contains a combination of key ingredients that work synergistically to provide relief and nourishment to the skin. The lotion is rich in Magnesium Chloride, a mineral known for its muscle relaxing properties. Magnesium Chloride helps to reduce muscle tension, ease cramps, and promote relaxation. Additionally, it aids in the absorption of magnesium into the body, which is essential for maintaining healthy muscle function. Aloe Vera is included in the formula for its soothing and moisturizing properties. It helps to calm irritated skin, reduce inflammation, and provide hydration. Shea Butter and Coconut Oil are both deeply moisturizing ingredients that help to nourish and soften the skin. They create a protective barrier that locks in moisture, leaving the skin feeling smooth and supple. Vitamin E Oil is added for its antioxidant properties, which help to protect the skin from free radicals and environmental damage. Overall, this Magnesium Lotion combines the benefits of Magnesium Chloride, Aloe Vera, Shea Butter, Coconut Oil, and Vitamin E Oil to provide relief from muscle pain and leg cramps while simultaneously nourishing and hydrating the skin.\"*\r\n\r\n```sql\r\nSELECT PROD_DESC_CHATGPT35_V1('Tandoori Mixed Grill') as response;\r\n```\r\n\r\n***Sample response:*** *\"Tandoori Mixed Grill is not a cosmetic product, but rather a popular Indian dish consisting of a variety of marinated meats and vegetables cooked in a traditional clay oven called a tandoor. It typically includes ingredients such as chicken, lamb, fish, or shrimp, along with a blend of spices and yogurt-based marinade. The marinade is made using a combination of ingredients like ginger, garlic, turmeric, cumin, coriander, paprika, garam masala, and yogurt, which help to tenderize the meat and infuse it with rich flavors. The capabilities of Tandoori Mixed Grill lie in its ability to provide a delicious and aromatic culinary experience. The combination of spices and yogurt-based marinade not only enhances the taste of the meats and vegetables but also helps to tenderize and moisten them during the cooking process. The high heat of the tandoor oven imparts a smoky and charred flavor to the dish, giving it a unique and authentic taste. Tandoori Mixed Grill is often served with naan bread, rice, or a side salad, making it a satisfying and flavorful meal option.\"*\r\n\r\nOur dataset contains only cosmetic products. So we want the LLM to generate descriptions for cosmetic products only. So let us tweak our prompt to achieve this.\r\n\r\nAfter tweaking the prompt, let's create a new function `PROD_DESC_CHATGPT35_V2`.\r\n\r\n```sql\r\nCREATE OR REPLACE FUNCTION PROD_DESC_CHATGPT35_V2(query varchar)\r\nRETURNS STRING\r\nLANGUAGE PYTHON\r\nRUNTIME_VERSION = 3.9\r\nHANDLER = 'complete_me'\r\nEXTERNAL_ACCESS_INTEGRATIONS = (vino_external_access_int)\r\nSECRETS = ('openai_key' = vino_open_ai_api)\r\nPACKAGES = ('openai')\r\nAS\r\n$$\r\nimport _snowflake\r\nimport openai\r\n\r\nopenai.api_key = _snowflake.get_generic_secret_string('openai_key')\r\nmodel=\"gpt-3.5-turbo\"\r\nprompt=\"Explain in detail what the ingredients and capabilities of this cosmetic product are. Limit the response to 150 words only. Respond with OOPS!! it is not a beauty product if the product in question is not a cosmetic or a beauty product. Here is the product:\"\r\n\r\ndef complete_me(QUERY):\r\n    messages=[\r\n    {'role': 'user', 'content':f\"{prompt} {QUERY}\"}\r\n    ]\r\n    response = openai.ChatCompletion.create(model=model,messages=messages,temperature=0)    \r\n    return response.choices[0].message[\"content\"]\r\n$$;\r\n```\r\n\r\nLet's call this function with the same sample queries again:\r\n\r\n```sql\r\nSELECT PROD_DESC_CHATGPT35_V2('Magnesium Lotion With Aloe Vera, Shea Butter, Coconut Oil & Magnesium Oil For Muscle Pain & Leg Cramps – Rich In Magnesium Chloride And Vitamin E Oil') as response;\r\n```\r\n\r\n***Sample response:*** *\"The Magnesium Lotion with Aloe Vera, Shea Butter, Coconut Oil, and Magnesium Oil is a beauty product designed to alleviate muscle pain and leg cramps. It contains a combination of ingredients that work synergistically to provide relief and nourishment to the skin.\r\n\r\nMagnesium chloride is a key ingredient in this lotion, known for its ability to relax muscles and reduce muscle pain. It is easily absorbed through the skin, making it an effective topical treatment. Aloe Vera is included for its soothing and moisturizing properties, helping to calm irritated skin and promote healing. Shea butter and coconut oil are both rich in essential fatty acids and vitamins, providing deep hydration and nourishment to the skin. These ingredients help to improve skin elasticity and promote a healthy, youthful appearance. Vitamin E oil is added for its antioxidant properties, protecting the skin from free radicals and environmental damage. It also aids in reducing inflammation and promoting skin healing. Overall, this Magnesium Lotion combines the benefits of magnesium chloride, aloe vera, shea butter, coconut oil, and vitamin E oil to provide relief from muscle pain and leg cramps while nourishing and rejuvenating the skin.\"*\r\n\r\n```sql\r\nSELECT PROD_DESC_CHATGPT35_V2('Tandoori Mixed Grill') as response;\r\n```\r\n\r\n***Sample response:*** *\"OOPS!! it is not a beauty product. The Tandoori Mixed Grill is not a cosmetic or beauty product. It is a popular Indian dish that consists of a variety of marinated and grilled meats and vegetables. The ingredients typically include chicken, lamb, and/or shrimp, along with a mixture of spices and yogurt-based marinade.The capabilities of the Tandoori Mixed Grill are centered around its delicious taste and flavor. The meats and vegetables are marinated in a blend of spices, such as cumin, coriander, turmeric, paprika, and garam masala, which gives them a rich and aromatic profile. The yogurt-based marinade helps to tenderize the meats and adds a creamy texture to the dish. When cooked in a tandoor oven or on a grill, the Tandoori Mixed Grill develops a smoky and charred flavor, while still retaining its juicy and tender texture. It is often served with naan bread, rice, and a side of mint chutney or raita. Overall, the Tandoori Mixed Grill is a flavorful and satisfying dish that showcases the vibrant and aromatic flavors of Indian cuisine.\"*\r\n\r\nInteresting! For non-cosmetic products, the response is not right. Now let's generate product descriptions for all the products in our products table.\r\n\r\nWe have played around with only 2 versions of the prompts. You can iterate on the prompts as many times as you want until the model produces desired output.\r\n\r\n### Prompt Engineering with GPT-4 model\r\n\r\nNow, let us repeat the experiment but using GPT-4 model instead.\r\n\r\nLet us start with defining the `PROD_DESC_CHATGPT4_V1` function:\r\n\r\n```sql\r\nCREATE OR REPLACE FUNCTION PROD_DESC_CHATGPT4_V1(query varchar)\r\nRETURNS STRING\r\nLANGUAGE PYTHON\r\nRUNTIME_VERSION = 3.9\r\nHANDLER = 'complete_me'\r\nEXTERNAL_ACCESS_INTEGRATIONS = (vino_external_access_int)\r\nSECRETS = ('openai_key' = vino_open_ai_api)\r\nPACKAGES = ('openai')\r\nAS\r\n$$\r\nimport _snowflake\r\nimport openai\r\ndef complete_me(QUERY):\r\n    openai.api_key = _snowflake.get_generic_secret_string('openai_key')\r\n    messages = [{\"role\": \"system\", \"content\": \"Explain in details what the ingredients and capabilities of this cosmetic product are. Limit the response to 150 words only. Here is the product: \"}, {\"role\": \"user\", \"content\": QUERY}]\r\n    model=\"gpt-4\"\r\n    response = openai.ChatCompletion.create(model=model,messages=messages,temperature=0,)    \r\n    return response.choices[0].message[\"content\"]\r\n$$;\r\n```\r\n\r\nLet's call this function with the same sample queries again:\r\n\r\n```sql\r\nSELECT PROD_DESC_CHATGPT4_V1('Magnesium Lotion With Aloe Vera, Shea Butter, Coconut Oil & Magnesium Oil For Muscle Pain & Leg Cramps – Rich In Magnesium Chloride And Vitamin E Oil') as response;\r\n```\r\n\r\n***Sample response:*** *\"This cosmetic product is a magnesium lotion enriched with Aloe Vera, Shea Butter, Coconut Oil, Magnesium Oil, Magnesium Chloride, and Vitamin E Oil. Magnesium Oil and Magnesium Chloride are known for their muscle relaxation properties, helping to alleviate muscle pain and leg cramps. Aloe Vera is renowned for its soothing and moisturizing capabilities, making the skin soft and supple. Shea Butter is a natural emollient that nourishes the skin deeply, while Coconut Oil provides hydration and prevents dryness. Vitamin E Oil is a powerful antioxidant that protects the skin from environmental damage and promotes healing. Together, these ingredients work synergistically to provide relief from muscle discomfort while enhancing the skin's overall health and appearance.\"*\r\n\r\n```sql\r\nSELECT PROD_DESC_CHATGPT4_V1('Tandoori Mixed Grill') as response;\r\n```\r\n\r\n***Sample response:*** *\"It seems there's been a misunderstanding. Tandoori Mixed Grill is not a cosmetic product. It's a popular Indian dish that includes a variety of marinated meats like chicken, lamb, and shrimp, typically cooked in a tandoor, a traditional clay oven. The marinade usually contains yogurt and spices like turmeric, cumin, and coriander. However, if you have a cosmetic product you'd like information on, feel free to provide its details.\"*\r\n\r\nThe responses look appropriate. How can we tune the prompts even better?\r\n\r\nLet's run the experiment again with an updated prompt, and define the `PROD_DESC_CHATGPT4_V2` function:\r\n\r\n```sql\r\nCREATE OR REPLACE FUNCTION PROD_DESC_CHATGPT4_V2(query varchar)\r\nRETURNS STRING\r\nLANGUAGE PYTHON\r\nRUNTIME_VERSION = 3.9\r\nHANDLER = 'complete_me'\r\nEXTERNAL_ACCESS_INTEGRATIONS = (vino_external_access_int)\r\nSECRETS = ('openai_key' = vino_open_ai_api)\r\nPACKAGES = ('openai')\r\nAS\r\n$$\r\nimport _snowflake\r\nimport openai\r\ndef complete_me(QUERY):\r\n    openai.api_key = _snowflake.get_generic_secret_string('openai_key')\r\n    messages = [{\"role\": \"system\", \"content\": \"You are an AI assistant who only knows about different cosmetic products and can explain in detail what the ingredients and capabilities of the product are. Limit the response to 150 words only. Here is the product: \"}, {\"role\": \"user\", \"content\": QUERY}]\r\n    model=\"gpt-4\"\r\n    response = openai.ChatCompletion.create(model=model,messages=messages,temperature=0,)    \r\n    return response.choices[0].message[\"content\"]\r\n$$;\r\n```\r\n\r\nLet's call this function with the same sample queries again:\r\n\r\n```sql\r\nSELECT PROD_DESC_CHATGPT4_V2('Magnesium Lotion With Aloe Vera, Shea Butter, Coconut Oil & Magnesium Oil For Muscle Pain & Leg Cramps – Rich In Magnesium Chloride And Vitamin E Oil') as response;\r\n```\r\n\r\n***Sample response:*** *\"Magnesium Lotion with Aloe Vera, Shea Butter, Coconut Oil & Magnesium Oil is a multi-functional cosmetic product. Magnesium Chloride, its key ingredient, is known for relieving muscle pain and leg cramps. Aloe Vera, a natural moisturizer, soothes and hydrates the skin. Shea Butter, rich in vitamins A and E, provides deep nourishment and promotes skin elasticity. Coconut Oil, packed with fatty acids, offers intense hydration and has anti-inflammatory properties. Magnesium Oil aids in muscle relaxation and helps improve skin health. Vitamin E Oil, a powerful antioxidant, protects the skin from damage and promotes healing. This lotion is a blend of natural ingredients designed to provide relief from muscle discomfort while enhancing skin health and vitality.\"*\r\n\r\n```sql\r\nSELECT PROD_DESC_CHATGPT4_V2('Tandoori Mixed Grill') as response;\r\n```\r\n\r\n***Sample response:*** *\"I'm sorry for the confusion, but as an AI assistant specializing in cosmetic products, I can't provide detailed information about food items like Tandoori Mixed Grill. My expertise lies in explaining the ingredients and capabilities of cosmetic products such as skincare, makeup, haircare, and more. If you have any questions about a specific cosmetic product, feel free to ask!\"*\r\n\r\n### Comparing GPT-4 model responses from prompt engineering\r\n\r\nAfter we experiment with different variation of prompts, we need a human in the loop to evaluate the LLM responses for each prompt.\r\n\r\nSo let's save the product names and the product descriptions generated by different model prompts in a Snowflake table. Copy, Paste and run the following query in your SQL worksheet to create the `PRODUCT_DESC_GPT4` table.\r\n\r\n```sql\r\nCREATE OR REPLACE TABLE PRODUCT_DESC_GPT4\r\nAS\r\nSELECT product_name, \r\n    PROD_DESC_CHATGPT4_V1(product_name) as DESC_V1,\r\n    PROD_DESC_CHATGPT4_V2(product_name) as DESC_V2\r\nFROM PRODUCT;\r\n\r\nSELECT * \r\nFROM PRODUCT_DESC_GPT4;\r\n```\r\n\r\n---\r\n\r\n![Product Description](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/prompt-engineering-and-llm-evaluation/product_desc.png)\r\n\r\n---\r\n\r\nLet us also add a `review` column to the table to store the human reviews of model responses.\r\n\r\n```sql\r\nALTER TABLE PRODUCT_DESC_GPT4 ADD REVIEW STRING(2);\r\n```\r\n\r\nFun part is you can work with Snowflake data and OpenAI models together using Snowflake External Access functionality.\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n\r\n## LLM Evaluation with Human Feedback\r\n\r\n\r\nFollow these steps to build a Streamlit application in Snowsight.\r\n\r\n**Step 1.** Click on **Streamlit** on the left navigation menu\r\n\r\n**Step 2.** Click on **+ Streamlit App** on the top right\r\n\r\n**Step 3.** Enter **App name**\r\n\r\n**Step 4.** Select **Warehouse** (VINO_CUSTOMER_EXP_WH_M) and **App location** (Database as CUSTOMER_EXP_DB and Schema as CUSTOMER_EXP_SCHEMA)\r\n\r\n**Step 5.** Click on **Create**\r\n\r\n- At this point, you will be provided code for an example Streamlit application\r\n\r\n**Step 6.** Replace sample application code displayed in the code editor on the left with the code provided in [llm_evaluation_sis_app.py](https://github.com/Snowflake-Labs/sfguide-prompt-engineering-and-llm-evaluation/blob/main/llm_evaluation_sis_app.py))\r\n\r\n**Step 7.** Click on **Run** on the top right\r\n\r\nIf all goes well, you should see the application in Snowsight as shown below.\r\n\r\n---\r\n\r\n![Streamlit-in-Snowflake](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/prompt-engineering-and-llm-evaluation/app_sis.png)\r\n\r\n---\r\n\r\n**Step 8.** In the Streamlit application, you can see the product name along with two different model responses. You can click on the description to read the entire text and write your feedback in the `review` column.\r\n\r\n**Step 9.** Once you are done reviewing the responses, you could click on `Update Reviews` button to save the reviews in the `PRODUCT_DESC_GPT4` table.\r\n\r\nVoila. It's done!\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n\r\n## Conclusion and Resources\r\n\r\n\r\nCongratulations! You have successfully learnt the basics of prompt engineering on your Large Language Models (LLMs) and to evaluate the responses of different LLMs through human feedback by building an interative Streamlit Application.\r\n\r\n### What you learned\r\n\r\n- How to access data from Snowflake Marketplace and use it for your analysis\r\n- How to use External Access to securely connect to the OpenAI API from Snowpark\r\n- How to use different prompts on a Large Language Model and capture model responses in a Snowflake table\r\n- How to build a Streamlit App to compare the model responses and capture the review in a Snowflake table\r\n\r\n### Related Resources\r\n\r\nWant to learn more about the tools and technologies used by your app? Check out the following resources:\r\n\r\n- [Source Code on GitHub](https://github.com/Snowflake-Labs/sfguide-prompt-engineering-and-llm-evaluation)\r\n- [External Access in Snowflake](https://docs.snowflake.com/en/developer-guide/external-network-access/external-network-access-overview)\r\n- [OpenAI's Completions API](https://platform.openai.com/docs/api-reference/completions)\r\n- [Demo on Snowflake Demo Hub](https://developers.snowflake.com/demos/generative-ai-llm-snowflake/)\r\n- [Build powerful generative AI apps with Streamlit](https://streamlit.io/generative-ai)","title":"Quickstart Article Body","dataType":"string","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"title":"Quickstart Article Logo Image","dataType":"string","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-1ec0d63338","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"id":"container-7f7192371c","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-3e3ee92ce4","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2024-07-08",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-8c1a5445c5","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. It may be out of date with current Snowflake instances\u003C/span\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"}},":itemsOrder":["quickstart_last_modi","text"]},"flexible_column_content_container_2":{"id":"container-9a2e9212eb","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{},":itemsOrder":[]},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"}},":itemsOrder":["contentfragment","flexible_column_cont"]},"flexible_column_content_container_2":{"id":"container-cad1246ec0","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_table_of_":{"id":"container-abc0a31e3b","layout":"SIMPLE","isDeveloperGuidesPage":false,":type":"snowflake-site/components/quickstart/quickstart-table-of-content/quickstart-table-of-content-container",":items":{"quickstart_table_of_":{"id":"quickstart-table-of-content-21c8eb0d3b","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/prompt-engineering-and-llm-evaluation",":type":"snowflake-site/components/quickstart/quickstart-table-of-content","headings":["\u003Ch2\u003EOverview\u003C/h2\u003E","\u003Ch2\u003ESet 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