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hands-on lab you will go from data consumer to AI builder. Starting from a fresh Snowflake trial account — with no uploads, no external APIs, and no data movement — you will build two AI-powered applications:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EA \u003Cstrong\u003ERetrieval-Augmented Generation (RAG)\u003C/strong\u003E app that answers questions from a corpus of text documents\u003C/li\u003E\u003Cli\u003EA \u003Cstrong\u003EText-to-SQL\u003C/strong\u003E interface that answers business questions from structured tables in plain English\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EAlong the way, you will use \u003Cstrong\u003ESnowflake CoCo\u003C/strong\u003E, Snowflake's AI coding assistant, to write and accelerate the most complex parts of the lab.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Snowflake Trial Account (any cloud region). \u003Ca href=\"https://signup.snowflake.com/\"\u003ESign up here\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EBasic familiarity with SQL\u003C/li\u003E\u003Cli\u003ENo Python experience required for Module 1; basic Python familiarity helps for Module 2\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to call large language models (LLMs) directly in Snowflake using \u003Ccode\u003ECORTEX.COMPLETE\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EThe architecture behind Retrieval-Augmented Generation (RAG) and why it prevents hallucinations\u003C/li\u003E\u003Cli\u003EHow to build a production RAG app using \u003Ccode\u003ECORTEX SEARCH SERVICE\u003C/code\u003E and a Snowflake Notebook\u003C/li\u003E\u003Cli\u003EHow to use Snowflake CoCo to generate complex Snowflake SQL and Python from plain English\u003C/li\u003E\u003Cli\u003EHow to evaluate your AI application's response quality with Snowsight Evaluations\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Snowflake Trial Account — \u003Ca href=\"https://signup.snowflake.com/\"\u003Esignup.snowflake.com\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EA web browser (Chrome or Firefox recommended)\u003C/li\u003E\u003Cli\u003ENo local software installation required\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA \u003Cstrong\u003Etext corpus pipeline\u003C/strong\u003E — ingest, chunk, and index text documents inside Snowflake\u003C/li\u003E\u003Cli\u003EA \u003Cstrong\u003ECortex Search Service\u003C/strong\u003E that supports hybrid keyword + semantic search\u003C/li\u003E\u003Cli\u003EA \u003Cstrong\u003EPython RAG application\u003C/strong\u003E that retrieves context and generates grounded answers\u003C/li\u003E\u003Cli\u003EA reusable \u003Cstrong\u003Eprompt pattern\u003C/strong\u003E for Text-to-SQL using sample data pre-loaded in your trial account\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ELab Files\u003C/h3\u003E\n","\u003Cp\u003EThis lab includes a pre-built Snowflake Notebook (\u003Ccode\u003Ebuilding-ai-apps-snowflake-cortex.ipynb\u003C/code\u003E) with all Python cells ready to run. You will upload it in the next section — no manual cell creation required.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESet Up Your Environment\u003C/h2\u003E\n","\u003Cp\u003EAll setup runs in a \u003Cstrong\u003ESQL Worksheet\u003C/strong\u003E in Snowsight. Navigate to **Left Pane > Create (+) Button ** and click \u003Cstrong\u003ESQL File\u003C/strong\u003E to create a new worksheet.\u003C/p\u003E\n","\u003Ch3\u003EStep 1: Enable Your Personal Database and Secondary Roles\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E--Find your USER IDENTIFIER. Paste this value in the CURRENT_USER field.\nSELECT CURRENT_USER();\n\n-- Enable secondary roles for permission inheritance. Replace CURRENT_USER in the script with the USER IDENTIFIER from the previous step.\nALTER USER CURRENT_USER SET DEFAULT_SECONDARY_ROLES = ('ALL');\n\n-- Activate secondary roles in the current session\nUSE SECONDARY ROLES ALL;\n\n-- Enable a Personal Database for Private Notebooks\nALTER ACCOUNT SET ENABLE_PERSONAL_DATABASE = TRUE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 2: Create the Workshop Database and Schemas\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Create the main workshop database\nCREATE DATABASE IF NOT EXISTS AI_WORKSHOP_DB;\n\n-- Schema for our RAG / unstructured data pipeline\nCREATE SCHEMA IF NOT EXISTS AI_WORKSHOP_DB.RAG_DATA;\n\n-- Schema for structured analytics (Text-to-SQL)\nCREATE SCHEMA IF NOT EXISTS AI_WORKSHOP_DB.ANALYTICS;\n\nUSE DATABASE AI_WORKSHOP_DB;\nUSE SCHEMA RAG_DATA;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 3: Create a Warehouse\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE WAREHOUSE IF NOT EXISTS WORKSHOP_WH\n  WAREHOUSE_SIZE = 'XSMALL'\n  AUTO_SUSPEND   = 60\n  AUTO_RESUME    = TRUE;\n\nUSE WAREHOUSE WORKSHOP_WH;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E X-Small warehouses are sufficient for all steps in this lab. Cortex Search and Cortex Complete use serverless compute that does not consume warehouse credits.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EUpload the Workshop Notebook\u003C/h2\u003E\n","\u003Cp\u003EAll Python steps in this lab run from a single pre-built Snowflake Notebook. Upload it once and run cells sequentially — no manual cell creation required.\u003C/p\u003E\n","\u003Ch3\u003EStep 1: Upload the Notebook\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EIn Snowsight, navigate to \u003Cstrong\u003EProjects > Workspaces\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EClick on \u003Cstrong\u003E+ Add new > Upload files\u003C/strong\u003E and the select the downloaded \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/accelerate-app-dev-cortex-code/assets/accelerate-app-dev-cortex-code.ipynb\"\u003E\u003Ccode\u003Eaccelerate-app-dev-cortex-code.ipynb\u003C/code\u003E\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EClick on \u003Cstrong\u003EConnect\u003C/strong\u003E next to the \u003Cstrong\u003ERun\u003C/strong\u003E button and select \u003Cstrong\u003ECreate and connect\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EWait for connection to commplete\u003C/li\u003E\u003Cli\u003ESet \u003Cstrong\u003EWarehouse\u003C/strong\u003E to \u003Ccode\u003EWORKSHOP_WH\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EStep 2: Run the Setup Cell\u003C/h3\u003E\n","\u003Cp\u003ENow run the setup code cell in the notebook imports all libraries and sets the active database, schema, and warehouse. \u003Cstrong\u003ERun this cell before any other.\u003C/strong\u003E You should see:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESession ready.\n  Database : AI_WORKSHOP_DB\n  Schema   : RAG_DATA\n  Warehouse: WORKSHOP_WH\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf you see an error, confirm the SQL Worksheet setup steps completed successfully.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETip:\u003C/strong\u003E Run cells individually to follow along with the lab guide.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESnowflake CoCo: Your AI Pair Programmer\u003C/h2\u003E\n","\u003Cp\u003EBefore we write a single line of lab code, take 10 minutes to meet the tool that will accelerate every step that follows.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESnowflake CoCo\u003C/strong\u003E is Snowflake's AI coding assistant. It understands Snowflake's full API surface — every SQL function, DDL syntax, Cortex primitive, and Snowpark class — and it generates runnable code from plain-English descriptions. It is available directly in Snowsight on all trial accounts.\u003C/p\u003E\n","\u003Ch3\u003EOpening Snowflake CoCo in Snowsight\u003C/h3\u003E\n","\u003Cp\u003ENavigate back to your SQL Worksheet, look for the icon (sparkle ✦) in the right side. Click it to open the Snowflake CoCo chat panel.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETip:\u003C/strong\u003E Snowflake CoCo is context-aware. It can see your active database, schema, and table names, so your prompts don't need to be overly specific.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EDemo 1: Generate Setup SQL\u003C/h3\u003E\n","\u003Cp\u003EIn the Snowflake CoCo chat panel, type the following prompt exactly:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECreate an internal stage in the AI_WORKSHOP_DB database and RAG_DATA schema called DOCS_STAGE with directory\ntables enabled and Snowflake SSE encryption.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESnowflake CoCo will return the SQL below. Accept it and run the command in your worksheet:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE STAGE AI_WORKSHOP_DB.RAG_DATA.DOCS_STAGE\n  DIRECTORY = (ENABLE = TRUE)\n  ENCRYPTION = (TYPE = 'SNOWFLAKE_SSE');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see the text "Stage DOCS_STAGE created with directory tables enabled and Snowflake SSE encryption."\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat just happened?\u003C/strong\u003E You described what you wanted in plain English, and Snowflake CoCo produced syntactically correct, Snowflake-idiomatic SQL — including the less-obvious \u003Ccode\u003EDIRECTORY\u003C/code\u003E and \u003Ccode\u003EENCRYPTION\u003C/code\u003E parameters. Throughout this lab, use Snowflake CoCo whenever you encounter a function or DDL pattern you haven't seen before.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EDemo 2: Explore an Unknown Function\u003C/h3\u003E\n","\u003Cp\u003ENow try this prompt in the Snowflake CoCo panel:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EHow do I use SNOWFLAKE.CORTEX.SPLIT_TEXT_RECURSIVE_CHARACTER to chunk text into\npieces of 1500 characters with 200-character overlap?\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESnowflake CoCo will return a working example of SQL functions with explanation. Keep this pattern in mind — you'll use this exact function in Module 2.\u003C/p\u003E\n","\u003Ch3\u003EKey Principle\u003C/h3\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EUse Snowflake CoCo to \u003Cstrong\u003Egenerate the hard parts\u003C/strong\u003E and \u003Cstrong\u003Eunderstand why\u003C/strong\u003E — don't just accept output blindly. The best workflow is: describe → generate → read → run → iterate.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EModule 1 – Your AI Foundation\u003C/h2\u003E\n","\u003Ch3\u003EThe Two Types of AI Questions\u003C/h3\u003E\n","\u003Cp\u003EEvery AI application over data falls into one of two categories:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EQuestion Type\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EData Type\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETechnique\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"What does our policy say about refunds?"\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EText, PDFs, docs\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ERAG\u003C/strong\u003E — retrieve relevant chunks, ground the LLM\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"What were total sales last quarter?"\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETables, structured data\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EText-to-SQL\u003C/strong\u003E — translate to SQL, execute, return result\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EThis module covers the foundations. Module 2 builds the production version.\u003C/p\u003E\n","\u003Ch3\u003EStep 1: Your First LLM Call\u003C/h3\u003E\n","\u003Cp\u003EOpen your uploaded notebook and run the \u003Cstrong\u003EStep 1 cell\u003C/strong\u003E (Module 1 section).\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Emodel  = 'mistral-7b'\nprompt = 'In two sentences, explain the difference between structured and unstructured data.'\nprint(cortex.complete(model, prompt))\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou just called a hosted LLM running inside your Snowflake account. No API key, no data leaving your security perimeter.\u003C/p\u003E\n","\u003Cp\u003ERun the \u003Cstrong\u003Emodel comparison cell\u003C/strong\u003E below it to contrast \u003Ccode\u003Emistral-7b\u003C/code\u003E and \u003Ccode\u003Ellama3-70b\u003C/code\u003E side by side.\u003C/p\u003E\n","\u003Ch3\u003EStep 2: The Hallucination Problem\u003C/h3\u003E\n","\u003Cp\u003ERun the \u003Cstrong\u003EStep 2 cell\u003C/strong\u003E. The model is asked for a specific private financial figure it cannot know:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eresponse = cortex.complete('mistral-7b', 'What is the exact revenue figure for Snowflake in Q4 FY26?')\nprint(response)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe model guesses, hedges, or states something unverifiable. This is the \u003Cstrong\u003Ehallucination problem\u003C/strong\u003E — and it is why RAG exists.\u003C/p\u003E\n","\u003Ch3\u003EStep 3: RAG — Grounding Fixes Hallucination\u003C/h3\u003E\n","\u003Cp\u003EThe solution is to \u003Cstrong\u003Eretrieve\u003C/strong\u003E relevant facts from your own data and \u003Cstrong\u003Einject\u003C/strong\u003E them into the prompt as context. The LLM then answers using only what you provided.\u003C/p\u003E\n","\u003Cp\u003ERun the \u003Cstrong\u003EStep 3 cell\u003C/strong\u003E. It pulls the top 5 customers by revenue live from \u003Ccode\u003ESNOWFLAKE_SAMPLE_DATA.TPCH_SF1\u003C/code\u003E — the TPC-H order dataset pre-loaded in every trial account — and grounds the LLM with that data:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Etop_customers = session.sql("""\n    SELECT\n        c.C_NAME,\n        c.C_MKTSEGMENT,\n        ROUND(SUM(o.O_TOTALPRICE), 0)       AS TOTAL_SPENT,\n        COUNT(DISTINCT o.O_ORDERKEY)         AS NUM_ORDERS\n    FROM SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.CUSTOMER c\n    JOIN SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.ORDERS   o ON c.C_CUSTKEY = o.O_CUSTKEY\n    GROUP BY 1, 2\n    ORDER BY TOTAL_SPENT DESC\n    LIMIT 5\n""").to_pandas()\n\n# ... formats as context string and asks a grounded question\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECompare the grounded answer to Step 2. This is the RAG pattern: \u003Cstrong\u003Eyour data → context → accurate answer\u003C/strong\u003E.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EIn production, the context is not hand-crafted — it is retrieved automatically from a search index over your full corpus. Module 2 builds that.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 4: Create Your Text Corpus\u003C/h3\u003E\n","\u003Cp\u003ERun the \u003Cstrong\u003EStep 4 cell\u003C/strong\u003E in the notebook. It creates \u003Ccode\u003EFEATURE_DOCS\u003C/code\u003E and loads 15 Snowflake product feature descriptions using \u003Ccode\u003Esession.write_pandas()\u003C/code\u003E — no SQL cell or worksheet switch needed. You should see:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECorpus loaded: 15 documents\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis table is your document library — the raw material for the RAG app in Module 2.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EModule 2 – Production RAG with Cortex Search\u003C/h2\u003E\n","\u003Ch3\u003EArchitecture Overview\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003EFEATURE_DOCS table\n       │\n       ▼\n  Chunk the text            ← SPLIT_TEXT_RECURSIVE_CHARACTER\n       │\n       ▼\n  CHUNKED_DOCS table\n       │\n       ▼\n  Cortex Search Service     ← automatic embedding + hybrid index\n       │\n       ▼\n  Python RAG App            ← retrieve context → Complete LLM → answer\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 1: Chunk the Text\u003C/h3\u003E\n","\u003Cp\u003EReal documents are too long to fit in a single LLM prompt. We break them into overlapping chunks so the search index can return the most relevant piece. Cortex Search indexes chunks, not the whole document. The search service finds the most relevant chunk for a question. If you indexed full documents, you'd retrieve a wall of text (much of it irrelevant) and would send it all to the LLM. Precision in this case improves answer quality and provides you a higher groundedness score (covered later in this lab).\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESnowflake CoCo prompt to try first:\u003C/strong\u003E\u003Cbr\u003E\n\u003Cem\u003E"Chunk the content column in FEATURE_DOCS into 1500-character pieces with 200-character overlap using SPLIT_TEXT_RECURSIVE_CHARACTER and store in CHUNKED_DOCS"\u003C/em\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EClick to run the command provided by Snowflake CoCo. You should see it created 15 chunks across 15 features with each at most 1500 characters and a 200-character overlap between consecutive chunks.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EOur documents are short so most produce a single chunk. With real PDFs or long articles, you would see many more chunks per source document.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 2: Create the Cortex Search Service\u003C/h3\u003E\n","\u003Cp\u003EA single DDL statement creates a fully managed hybrid search index — Snowflake handles embedding, vectorization, and retrieval automatically.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESnowflake CoCo prompt to try first:\u003C/strong\u003E\u003Cbr\u003E\n\u003Cem\u003E"Create a Cortex Search Service called FEATURE_SEARCH_SERVICE on CHUNKED_DOCS.chunk_text, with feature_name as an attribute, using snowflake-arctic-embed-l-v2.0 and a 1-minute target lag"\u003C/em\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EClick to run the command provided by Snowflake CoCo. The FEATURE_SEARCH_SERVICE is created. The service will index automatically, but may take a few minutes to become active.\u003C/p\u003E\n","\u003Cp\u003EAsk Snowflake CoCo after a few minutes:\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cem\u003E"Is the service active and ready to query?"\u003C/em\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EIf you see that it is active for both indexing and serving with all 15 rows indexed and ready to query, you can proceed to the next step.\u003C/p\u003E\n","\u003Ch3\u003EStep 3: Build the RAG Application\u003C/h3\u003E\n","\u003Cp\u003ERun the \u003Cstrong\u003EStep 3 cell\u003C/strong\u003E in the notebook to define and instantiate \u003Ccode\u003ERAG_App\u003C/code\u003E.\u003C/p\u003E\n","\u003Ch3\u003EStep 4: Test Your Application\u003C/h3\u003E\n","\u003Cp\u003ERun the \u003Cstrong\u003EStep 4 cell\u003C/strong\u003E in the notebook. It asks 5 test questions against your RAG app and prints each answer. Each response is scoped entirely to your 15-row corpus — the application cannot hallucinate beyond what you gave it. Note this cell may take a few minutes to run.\u003C/p\u003E\n","\u003Ch3\u003EStep 5: Evaluate Response Quality\u003C/h3\u003E\n","\u003Cp\u003ERun the \u003Cstrong\u003EEvaluate Response Quality\u003C/strong\u003E quality step in the notebook.\u003C/p\u003E\n","\u003Cp\u003EThis uses Cortex LLM to score the quality of your RAG app's answers. For each test question, the service calls retrieve_context() to fetch the top 3 matching chunks from Cortex Search. Then, it calls generate_answer() to send those chunks and the question to mistral-large2 to produce an answer. It scores on three metrics: groundedness, context relevance, and answer relevance on a scale from 0.0 to 1.0 asking the LLM to judge.\u003C/p\u003E\n","\u003Cp\u003EA well-tuned RAG app should score \u003Cstrong\u003E> 0.8 on all metrics\u003C/strong\u003E. If Groundedness is low, tighten the prompt instructions. If Context Relevance is low, your search service or chunking needs improvement. If groundedness is low, your prompt isn't constraining the model tightly enough. If answer relevance is low, the model is retrieving the right context, but generating poor answers.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EModule 3 – Text-to-SQL with Sample Data\u003C/h2\u003E\n","\u003Cp\u003EThis module uses \u003Ccode\u003ESNOWFLAKE_SAMPLE_DATA\u003C/code\u003E — the TPC-H order dataset pre-loaded in every trial account — as structured data for a Text-to-SQL interface. All steps run from Python cells in the notebook; no worksheet switching is needed.\u003C/p\u003E\n","\u003Ch3\u003EStep 1: Explore the Schema\u003C/h3\u003E\n","\u003Cp\u003ERun the \u003Cstrong\u003Eschema exploration cell\u003C/strong\u003E in the Module 3 section of the notebook. It queries \u003Ccode\u003EINFORMATION_SCHEMA\u003C/code\u003E via \u003Ccode\u003Esession.sql()\u003C/code\u003E and prints the table list:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ETables in SNOWFLAKE_SAMPLE_DATA.TPCH_SF1:\nTABLE_NAME    ROW_COUNT\n  CUSTOMER      150,000\n  LINEITEM    6,001,215\n    NATION           25\n    ORDERS    1,500,000\n      PART      200,000\n  PARTSUPP      800,000\n    REGION            5\n  SUPPLIER       10,000\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 2: Build and Run Text-to-SQL\u003C/h3\u003E\n","\u003Cp\u003ERun the \u003Cstrong\u003E\u003Ccode\u003Eask_structured_data\u003C/code\u003E cell\u003C/strong\u003E. It:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ESends a natural-language question + the schema description to \u003Ccode\u003ECORTEX.COMPLETE\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EExtracts the generated SQL\u003C/li\u003E\u003Cli\u003EExecutes it against \u003Ccode\u003ESNOWFLAKE_SAMPLE_DATA\u003C/code\u003E via \u003Ccode\u003Esession.sql()\u003C/code\u003E and returns a DataFrame\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E For production accuracy, use \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst\"\u003ECortex Analyst\u003C/a\u003E with a YAML semantic model.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EFinally, run the \u003Cstrong\u003Eadditional questions cell\u003C/strong\u003E to test three more questions — all from the same notebook, no context switching.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion & Resources\u003C/h2\u003E\n","\u003Ch3\u003EWhat You Built\u003C/h3\u003E\n","\u003Cp\u003EIn this lab you built two fully functional AI applications from scratch on a Snowflake trial account:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EA \u003Cstrong\u003ERAG application\u003C/strong\u003E backed by Cortex Search that retrieves grounded answers from a text corpus — with no hallucination outside your data\u003C/li\u003E\u003Cli\u003EA \u003Cstrong\u003EText-to-SQL interface\u003C/strong\u003E that translates plain-English questions into executable Snowflake queries against real sample data\u003C/li\u003E\u003Cli\u003EUsed \u003Cstrong\u003ESnowflake CoCo\u003C/strong\u003E throughout to generate complex SQL and Python from natural language, cutting development time on the hardest parts of the lab\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to call LLMs with \u003Ccode\u003ECORTEX.COMPLETE\u003C/code\u003E in Python and SQL\u003C/li\u003E\u003Cli\u003EThe RAG architecture — retrieval → grounding → generation — and why it prevents hallucination\u003C/li\u003E\u003Cli\u003EHow to chunk text with \u003Ccode\u003ESPLIT_TEXT_RECURSIVE_CHARACTER\u003C/code\u003E and build a \u003Ccode\u003ECORTEX SEARCH SERVICE\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EHow to write a reusable RAG Python class using \u003Ccode\u003Esnowflake.core\u003C/code\u003E and \u003Ccode\u003Esnowflake.cortex\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EHow to use Snowflake CoCo to generate Snowflake-idiomatic SQL and Python from descriptions\u003C/li\u003E\u003Cli\u003EHow to evaluate application quality with Snowsight Evaluations\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ENext Steps\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EScale your corpus:\u003C/strong\u003E Upload real PDFs to a Snowflake Stage and use \u003Ccode\u003EPARSE_DOCUMENT\u003C/code\u003E to extract and chunk their contents\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAdd a UI:\u003C/strong\u003E Wrap your \u003Ccode\u003ERAG_App\u003C/code\u003E in a \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/streamlit/about-streamlit\"\u003EStreamlit in Snowflake\u003C/a\u003E app for a shareable chat interface\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EProduction Text-to-SQL:\u003C/strong\u003E Explore \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst\"\u003ECortex Analyst\u003C/a\u003E with a YAML semantic model for business-grade accuracy\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECombine both:\u003C/strong\u003E Build a unified agent that routes questions to Cortex Search or Cortex Analyst based on whether the question is about documents or structured tables\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/overview\"\u003ESnowflake Cortex Overview\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview\"\u003ECortex Search Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst\"\u003ECortex Analyst Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight/notebooks\"\u003ESnowflake Notebooks\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-copilot\"\u003ESnowflake CoCo (Snowflake Copilot)\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://quickstarts.snowflake.com/guide/getting_started_with_cortex_agents\"\u003EGetting Started with Cortex Agents — Quickstart\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/streamlit/about-streamlit\"\u003EStreamlit in Snowflake\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Building AI Applications with Snowflake Cortex: RAG, Text-to-SQL & Snowflake CoCo","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","value":"\u003C!-- ------------------------ --\u003E\n## Overview\n\nIn this hands-on lab you will go from data consumer to AI builder. Starting from a fresh Snowflake trial account — with no uploads, no external APIs, and no data movement — you will build two AI-powered applications:\n\n1. A **Retrieval-Augmented Generation (RAG)** app that answers questions from a corpus of text documents\n2. A **Text-to-SQL** interface that answers business questions from structured tables in plain English\n\nAlong the way, you will use **Snowflake CoCo**, Snowflake's AI coding assistant, to write and accelerate the most complex parts of the lab.\n\n### Prerequisites\n- A Snowflake Trial Account (any cloud region). [Sign up here](https://signup.snowflake.com/)\n- Basic familiarity with SQL\n- No Python experience required for Module 1; basic Python familiarity helps for Module 2\n\n### What You'll Learn\n- How to call large language models (LLMs) directly in Snowflake using `CORTEX.COMPLETE`\n- The architecture behind Retrieval-Augmented Generation (RAG) and why it prevents hallucinations\n- How to build a production RAG app using `CORTEX SEARCH SERVICE` and a Snowflake Notebook\n- How to use Snowflake CoCo to generate complex Snowflake SQL and Python from plain English\n- How to evaluate your AI application's response quality with Snowsight Evaluations\n\n### What You'll Need\n- A Snowflake Trial Account — [signup.snowflake.com](https://signup.snowflake.com/)\n- A web browser (Chrome or Firefox recommended)\n- No local software installation required\n\n### What You'll Build\n- A **text corpus pipeline** — ingest, chunk, and index text documents inside Snowflake\n- A **Cortex Search Service** that supports hybrid keyword + semantic search\n- A **Python RAG application** that retrieves context and generates grounded answers\n- A reusable **prompt pattern** for Text-to-SQL using sample data pre-loaded in your trial account\n\n### Lab Files\nThis lab includes a pre-built Snowflake Notebook (`building-ai-apps-snowflake-cortex.ipynb`) with all Python cells ready to run. You will upload it in the next section — no manual cell creation required.\n\n\u003C!-- ------------------------ --\u003E\n## Set Up Your Environment\n\n\nAll setup runs in a **SQL Worksheet** in Snowsight. Navigate to **Left Pane \u003E Create (+) Button ** and click **SQL File** to create a new worksheet.\n\n### Step 1: Enable Your Personal Database and Secondary Roles\n\n\n```sql\n--Find your USER IDENTIFIER. Paste this value in the CURRENT_USER field.\nSELECT CURRENT_USER();\n\n-- Enable secondary roles for permission inheritance. Replace CURRENT_USER in the script with the USER IDENTIFIER from the previous step.\nALTER USER CURRENT_USER SET DEFAULT_SECONDARY_ROLES = ('ALL');\n\n-- Activate secondary roles in the current session\nUSE SECONDARY ROLES ALL;\n\n-- Enable a Personal Database for Private Notebooks\nALTER ACCOUNT SET ENABLE_PERSONAL_DATABASE = TRUE;\n```\n\n### Step 2: Create the Workshop Database and Schemas\n\n```sql\n-- Create the main workshop database\nCREATE DATABASE IF NOT EXISTS AI_WORKSHOP_DB;\n\n-- Schema for our RAG / unstructured data pipeline\nCREATE SCHEMA IF NOT EXISTS AI_WORKSHOP_DB.RAG_DATA;\n\n-- Schema for structured analytics (Text-to-SQL)\nCREATE SCHEMA IF NOT EXISTS AI_WORKSHOP_DB.ANALYTICS;\n\nUSE DATABASE AI_WORKSHOP_DB;\nUSE SCHEMA RAG_DATA;\n```\n\n### Step 3: Create a Warehouse\n\n```sql\nCREATE WAREHOUSE IF NOT EXISTS WORKSHOP_WH\n  WAREHOUSE_SIZE = 'XSMALL'\n  AUTO_SUSPEND   = 60\n  AUTO_RESUME    = TRUE;\n\nUSE WAREHOUSE WORKSHOP_WH;\n```\n\n\u003E **Note:** X-Small warehouses are sufficient for all steps in this lab. Cortex Search and Cortex Complete use serverless compute that does not consume warehouse credits.\n\n\u003C!-- ------------------------ --\u003E\n## Upload the Workshop Notebook\n\nAll Python steps in this lab run from a single pre-built Snowflake Notebook. Upload it once and run cells sequentially — no manual cell creation required.\n\n### Step 1: Upload the Notebook\n\n1. In Snowsight, navigate to **Projects \u003E Workspaces**\n2. Click on **+ Add new \u003E Upload files** and the select the downloaded [`accelerate-app-dev-cortex-code.ipynb`](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/accelerate-app-dev-cortex-code/assets/accelerate-app-dev-cortex-code.ipynb)\n3. Click on **Connect** next to the **Run** button and select **Create and connect**\n4. Wait for connection to commplete\n6. Set **Warehouse** to `WORKSHOP_WH`\n\n### Step 2: Run the Setup Cell\n\nNow run the setup code cell in the notebook imports all libraries and sets the active database, schema, and warehouse. **Run this cell before any other.** You should see:\n\n```\nSession ready.\n  Database : AI_WORKSHOP_DB\n  Schema   : RAG_DATA\n  Warehouse: WORKSHOP_WH\n```\n\nIf you see an error, confirm the SQL Worksheet setup steps completed successfully.\n\n\u003E **Tip:** Run cells individually to follow along with the lab guide.\n\n\u003C!-- ------------------------ --\u003E\n## Snowflake CoCo: Your AI Pair Programmer\n\nBefore we write a single line of lab code, take 10 minutes to meet the tool that will accelerate every step that follows.\n\n**Snowflake CoCo** is Snowflake's AI coding assistant. It understands Snowflake's full API surface — every SQL function, DDL syntax, Cortex primitive, and Snowpark class — and it generates runnable code from plain-English descriptions. It is available directly in Snowsight on all trial accounts.\n\n### Opening Snowflake CoCo in Snowsight\n\nNavigate back to your SQL Worksheet, look for the icon (sparkle ✦) in the right side. Click it to open the Snowflake CoCo chat panel.\n\n\u003E **Tip:** Snowflake CoCo is context-aware. It can see your active database, schema, and table names, so your prompts don't need to be overly specific.\n\n### Demo 1: Generate Setup SQL\n\nIn the Snowflake CoCo chat panel, type the following prompt exactly:\n\n```\nCreate an internal stage in the AI_WORKSHOP_DB database and RAG_DATA schema called DOCS_STAGE with directory\ntables enabled and Snowflake SSE encryption.\n```\n\nSnowflake CoCo will return the SQL below. Accept it and run the command in your worksheet:\n\n```sql\nCREATE OR REPLACE STAGE AI_WORKSHOP_DB.RAG_DATA.DOCS_STAGE\n  DIRECTORY = (ENABLE = TRUE)\n  ENCRYPTION = (TYPE = 'SNOWFLAKE_SSE');\n```\nYou should see the text \"Stage DOCS_STAGE created with directory tables enabled and Snowflake SSE encryption.\"\n\n\u003E **What just happened?** You described what you wanted in plain English, and Snowflake CoCo produced syntactically correct, Snowflake-idiomatic SQL — including the less-obvious `DIRECTORY` and `ENCRYPTION` parameters. Throughout this lab, use Snowflake CoCo whenever you encounter a function or DDL pattern you haven't seen before.\n\n### Demo 2: Explore an Unknown Function\n\nNow try this prompt in the Snowflake CoCo panel:\n\n```\nHow do I use SNOWFLAKE.CORTEX.SPLIT_TEXT_RECURSIVE_CHARACTER to chunk text into\npieces of 1500 characters with 200-character overlap?\n```\n\nSnowflake CoCo will return a working example of SQL functions with explanation. Keep this pattern in mind — you'll use this exact function in Module 2.\n\n### Key Principle\n\n\u003E Use Snowflake CoCo to **generate the hard parts** and **understand why** — don't just accept output blindly. The best workflow is: describe → generate → read → run → iterate.\n\n\u003C!-- ------------------------ --\u003E\n## Module 1 – Your AI Foundation\n\n\n### The Two Types of AI Questions\n\nEvery AI application over data falls into one of two categories:\n\n| Question Type | Data Type | Technique |\n|---|---|---|\n| \"What does our policy say about refunds?\" | Text, PDFs, docs | **RAG** — retrieve relevant chunks, ground the LLM |\n| \"What were total sales last quarter?\" | Tables, structured data | **Text-to-SQL** — translate to SQL, execute, return result |\n\nThis module covers the foundations. Module 2 builds the production version.\n\n### Step 1: Your First LLM Call\n\nOpen your uploaded notebook and run the **Step 1 cell** (Module 1 section).\n\n```python\nmodel  = 'mistral-7b'\nprompt = 'In two sentences, explain the difference between structured and unstructured data.'\nprint(cortex.complete(model, prompt))\n```\n\nYou just called a hosted LLM running inside your Snowflake account. No API key, no data leaving your security perimeter.\n\nRun the **model comparison cell** below it to contrast `mistral-7b` and `llama3-70b` side by side.\n\n### Step 2: The Hallucination Problem\n\nRun the **Step 2 cell**. The model is asked for a specific private financial figure it cannot know:\n\n```python\nresponse = cortex.complete('mistral-7b', 'What is the exact revenue figure for Snowflake in Q4 FY26?')\nprint(response)\n```\n\nThe model guesses, hedges, or states something unverifiable. This is the **hallucination problem** — and it is why RAG exists.\n\n### Step 3: RAG — Grounding Fixes Hallucination\n\nThe solution is to **retrieve** relevant facts from your own data and **inject** them into the prompt as context. The LLM then answers using only what you provided.\n\nRun the **Step 3 cell**. It pulls the top 5 customers by revenue live from `SNOWFLAKE_SAMPLE_DATA.TPCH_SF1` — the TPC-H order dataset pre-loaded in every trial account — and grounds the LLM with that data:\n\n```python\ntop_customers = session.sql(\"\"\"\n    SELECT\n        c.C_NAME,\n        c.C_MKTSEGMENT,\n        ROUND(SUM(o.O_TOTALPRICE), 0)       AS TOTAL_SPENT,\n        COUNT(DISTINCT o.O_ORDERKEY)         AS NUM_ORDERS\n    FROM SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.CUSTOMER c\n    JOIN SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.ORDERS   o ON c.C_CUSTKEY = o.O_CUSTKEY\n    GROUP BY 1, 2\n    ORDER BY TOTAL_SPENT DESC\n    LIMIT 5\n\"\"\").to_pandas()\n\n# ... formats as context string and asks a grounded question\n```\n\nCompare the grounded answer to Step 2. This is the RAG pattern: **your data → context → accurate answer**.\n\n\u003E In production, the context is not hand-crafted — it is retrieved automatically from a search index over your full corpus. Module 2 builds that.\n\n### Step 4: Create Your Text Corpus\n\nRun the **Step 4 cell** in the notebook. It creates `FEATURE_DOCS` and loads 15 Snowflake product feature descriptions using `session.write_pandas()` — no SQL cell or worksheet switch needed. You should see:\n\n```\nCorpus loaded: 15 documents\n```\n\nThis table is your document library — the raw material for the RAG app in Module 2.\n\n\u003C!-- ------------------------ --\u003E\n## Module 2 – Production RAG with Cortex Search\n\n\n### Architecture Overview\n\n```\nFEATURE_DOCS table\n       │\n       ▼\n  Chunk the text            ← SPLIT_TEXT_RECURSIVE_CHARACTER\n       │\n       ▼\n  CHUNKED_DOCS table\n       │\n       ▼\n  Cortex Search Service     ← automatic embedding + hybrid index\n       │\n       ▼\n  Python RAG App            ← retrieve context → Complete LLM → answer\n```\n\n### Step 1: Chunk the Text\n\nReal documents are too long to fit in a single LLM prompt. We break them into overlapping chunks so the search index can return the most relevant piece. Cortex Search indexes chunks, not the whole document. The search service finds the most relevant chunk for a question. If you indexed full documents, you'd retrieve a wall of text (much of it irrelevant) and would send it all to the LLM. Precision in this case improves answer quality and provides you a higher groundedness score (covered later in this lab).\n\n\u003E **Snowflake CoCo prompt to try first:**  \n\u003E *\"Chunk the content column in FEATURE_DOCS into 1500-character pieces with 200-character overlap using SPLIT_TEXT_RECURSIVE_CHARACTER and store in CHUNKED_DOCS\"*\n\nClick to run the command provided by Snowflake CoCo. You should see it created 15 chunks across 15 features with each at most 1500 characters and a 200-character overlap between consecutive chunks.\n\n\n\u003E Our documents are short so most produce a single chunk. With real PDFs or long articles, you would see many more chunks per source document.\n\n### Step 2: Create the Cortex Search Service\n\nA single DDL statement creates a fully managed hybrid search index — Snowflake handles embedding, vectorization, and retrieval automatically.\n\n\u003E **Snowflake CoCo prompt to try first:**  \n\u003E *\"Create a Cortex Search Service called FEATURE_SEARCH_SERVICE on CHUNKED_DOCS.chunk_text, with feature_name as an attribute, using snowflake-arctic-embed-l-v2.0 and a 1-minute target lag\"*\n\nClick to run the command provided by Snowflake CoCo. The FEATURE_SEARCH_SERVICE is created. The service will index automatically, but may take a few minutes to become active. \n\nAsk Snowflake CoCo after a few minutes: \n\u003E *\"Is the service active and ready to query?\"*\n\nIf you see that it is active for both indexing and serving with all 15 rows indexed and ready to query, you can proceed to the next step. \n\n### Step 3: Build the RAG Application\n\nRun the **Step 3 cell** in the notebook to define and instantiate `RAG_App`.\n\n### Step 4: Test Your Application\n\nRun the **Step 4 cell** in the notebook. It asks 5 test questions against your RAG app and prints each answer. Each response is scoped entirely to your 15-row corpus — the application cannot hallucinate beyond what you gave it. Note this cell may take a few minutes to run. \n\n### Step 5: Evaluate Response Quality\n\nRun the **Evaluate Response Quality** quality step in the notebook. \n\nThis uses Cortex LLM to score the quality of your RAG app's answers. For each test question, the service calls retrieve_context() to fetch the top 3 matching chunks from Cortex Search. Then, it calls generate_answer() to send those chunks and the question to mistral-large2 to produce an answer. It scores on three metrics: groundedness, context relevance, and answer relevance on a scale from 0.0 to 1.0 asking the LLM to judge. \n\nA well-tuned RAG app should score **\u003E 0.8 on all metrics**. If Groundedness is low, tighten the prompt instructions. If Context Relevance is low, your search service or chunking needs improvement. If groundedness is low, your prompt isn't constraining the model tightly enough. If answer relevance is low, the model is retrieving the right context, but generating poor answers. \n\n\u003C!-- ------------------------ --\u003E\n## Module 3 – Text-to-SQL with Sample Data\n\nThis module uses `SNOWFLAKE_SAMPLE_DATA` — the TPC-H order dataset pre-loaded in every trial account — as structured data for a Text-to-SQL interface. All steps run from Python cells in the notebook; no worksheet switching is needed.\n\n### Step 1: Explore the Schema\n\nRun the **schema exploration cell** in the Module 3 section of the notebook. It queries `INFORMATION_SCHEMA` via `session.sql()` and prints the table list:\n\n```\nTables in SNOWFLAKE_SAMPLE_DATA.TPCH_SF1:\nTABLE_NAME    ROW_COUNT\n  CUSTOMER      150,000\n  LINEITEM    6,001,215\n    NATION           25\n    ORDERS    1,500,000\n      PART      200,000\n  PARTSUPP      800,000\n    REGION            5\n  SUPPLIER       10,000\n```\n\n### Step 2: Build and Run Text-to-SQL\n\nRun the **`ask_structured_data` cell**. It:\n1. Sends a natural-language question + the schema description to `CORTEX.COMPLETE`\n2. Extracts the generated SQL\n3. Executes it against `SNOWFLAKE_SAMPLE_DATA` via `session.sql()` and returns a DataFrame\n\n\u003E **Note:** For production accuracy, use [Cortex Analyst](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst) with a YAML semantic model.\n\nFinally, run the **additional questions cell** to test three more questions — all from the same notebook, no context switching.\n\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion & Resources\n\n\n### What You Built\n\nIn this lab you built two fully functional AI applications from scratch on a Snowflake trial account:\n\n- A **RAG application** backed by Cortex Search that retrieves grounded answers from a text corpus — with no hallucination outside your data\n- A **Text-to-SQL interface** that translates plain-English questions into executable Snowflake queries against real sample data\n- Used **Snowflake CoCo** throughout to generate complex SQL and Python from natural language, cutting development time on the hardest parts of the lab\n\n### What You Learned\n\n- How to call LLMs with `CORTEX.COMPLETE` in Python and SQL\n- The RAG architecture — retrieval → grounding → generation — and why it prevents hallucination\n- How to chunk text with `SPLIT_TEXT_RECURSIVE_CHARACTER` and build a `CORTEX SEARCH SERVICE`\n- How to write a reusable RAG Python class using `snowflake.core` and `snowflake.cortex`\n- How to use Snowflake CoCo to generate Snowflake-idiomatic SQL and Python from descriptions\n- How to evaluate application quality with Snowsight Evaluations\n\n### Next Steps\n\n- **Scale your corpus:** Upload real PDFs to a Snowflake Stage and use `PARSE_DOCUMENT` to extract and chunk their contents\n- **Add a UI:** Wrap your `RAG_App` in a [Streamlit in Snowflake](https://docs.snowflake.com/en/developer-guide/streamlit/about-streamlit) app for a shareable chat interface\n- **Production Text-to-SQL:** Explore [Cortex Analyst](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst) with a YAML semantic model for business-grade accuracy\n- **Combine both:** Build a unified agent that routes questions to Cortex Search or Cortex Analyst based on whether the question is about documents or structured tables\n\n### Related Resources\n\n- [Snowflake Cortex Overview](https://docs.snowflake.com/en/user-guide/snowflake-cortex/overview)\n- [Cortex Search Documentation](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview)\n- [Cortex Analyst Documentation](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst)\n- [Snowflake Notebooks](https://docs.snowflake.com/en/user-guide/ui-snowsight/notebooks)\n- [Snowflake CoCo (Snowflake Copilot)](https://docs.snowflake.com/en/user-guide/snowflake-copilot)\n- [Getting Started with Cortex Agents — Quickstart](https://quickstarts.snowflake.com/guide/getting_started_with_cortex_agents)\n- [Streamlit in Snowflake](https://docs.snowflake.com/en/developer-guide/streamlit/about-streamlit)\n","title":"Quickstart Article Body","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-b44fd2fd12","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-8048dea029","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-a3a734d069","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-05-26",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-6c147d9a22","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. 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