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This task, often referred to as “text-to-SQL,” was historically introduced as a coding challenge. However, it has proven to be incredibly difficult, as it involves complexities not only at the code layer but also on the level of business context. \u003Ca href=\"https://snowflakecloud.wpenginepowered.com/en/blog/cortex-analyst-ai-self-service-analytics/\" target=\"_blank\" rel=\"noreferrer noopener\"\u003ESnowflake Cortex Analyst\u003C/a\u003E addresses this challenge by introducing an agentic AI system, coupled with a semantic model to achieve an impressive 90%+ SQL accuracy on real-world use cases, ensuring business users get accurate and precise answers. \u003C/p\u003E\u003Cp\u003EBased on an internal evaluation mirroring real-world use cases, Cortex Analyst outperforms alternatives — nearly 2x as accurate as single-prompt SQL generation from GPT-4o and about 14% more accurate than other solutions on the market.\u003Cstrong\u003E \u003C/strong\u003EFor more details on our system, refer to our \u003Ca href=\"https://snowflakecloud.wpenginepowered.com/engineering-blog/snowflake-cortex-analyst-behind-the-scenes/\" target=\"_blank\" rel=\"noreferrer noopener\"\u003ECortex Analyst: Behind the Scenes\u003C/a\u003E blog post.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_1":{"id":"image-a347d0cdb6","alt":"Figure 1. 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SQL generation accuracy for Snowflake Cortex Analyst vs. alternatives",":type":"snowflake-site/components/image"},"blog_text_2":{"id":"blog-text-02e5a46a21","text":"\u003Cp\u003EIn this blog, we delve into the benchmarking and evaluation of Cortex Analyst, highlighting how it outperforms existing solutions in delivering accurate SQL generation for real-world business intelligence (BI) tasks.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"title_v2_1":{"id":"blog-title-f3b0091bab","propertiesId":"challenges-of-traditional-text-to-sql-benchmarks","type":"heading3","lines":["Challenges of traditional text-to-SQL benchmarks"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_3":{"id":"blog-text-00e3eed6ff","text":"\u003Cp\u003EWhat makes a system “accurate,” and how can we build confidence in its accuracy from the perspective of the end business user? Accuracy of text-to-SQL systems has traditionally been evaluated using popular SQL benchmarks like Spider and BIRD. While these benchmarks have offered researchers effective tools for experimentation, the gap between these benchmarks and real-world business use cases is significant.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_2":{"id":"image-d0e3497fe7","alt":"Figure 2. Considerations for real-world conversational self-serve analytics evaluation","lazyEnabled":true,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--bb209d0c-f7ff-470b-a7c5-1c94c7aa8c0d/figure-1-challenges.jpg?quality=85&preferwebp=true","height":"1141","width":"1466","title":"Figure 2. Considerations for real-world conversational self-serve analytics evaluation",":type":"snowflake-site/components/image"},"blog_text_4":{"id":"blog-text-f69afdf217","text":"Looking at Spider \u003Ca href=\"https://yale-lily.github.io/spider\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"\u003Eleaderboards\u003C/a\u003E, where top models achieve 90%+ accuracy and even solutions from a year ago surpass 80%, one might assume that text-to-SQL is a solved problem. So, if that's the case, why isn’t everyone already using LLM solutions to interact with data? \u003C/p\u003E\u003Cp\u003EThe reality is that when applied to real-world business use cases, these solutions often fall short. For instance, when we tested a state-of-the-art language model, GPT-4o, using our internal evaluation set, its accuracy plummeted to 51%. This significant gap between benchmark performance and real-world application reveals several limitations of traditional benchmarks. There are four main pillars that define the gap between nice 90% benchmark numbers vs. real-world BI use cases:\u003C/p\u003E\u003Cul class=\"wp-block-list\"\u003E\n\u003Cli\u003E\u003Cstrong\u003EQuestion complexity\u003C/strong\u003E: The natural language questions used in existing benchmarks are often not very complex, industry-specific or realistic. They also fail to capture the types of questions frequently asked in BI contexts. \u003C/li\u003E\n\n\n\n\u003Cli\u003E\u003Cstrong\u003ESchema complexity: \u003C/strong\u003EThe databases in these benchmarks don’t always represent the complex, messy and often unclear schemas that are common in BI tasks. In addition, time-series tables — which are very common in transactional data, like sales or user web activity — are also significantly underrepresented in existing benchmarks.\u003C/li\u003E\n\n\n\n\u003Cli\u003E\u003Cstrong\u003ESQL complexity\u003C/strong\u003E: These benchmarks often lack complex queries, such as those containing window functions, CTEs and complex aggregations, which are crucial for real-world BI tasks.\u003C/li\u003E\n\n\n\n\u003Cli\u003E\u003Cstrong\u003EMeasure SQL in the context of semantics: \u003C/strong\u003ETraditional benchmarks fail to account for the importance of aligning SQL queries with specific business definitions and metrics. For instance, how different organizations define key metrics like \"Daily Active Users\" can vary significantly, impacting the accuracy of SQL queries in real-world applications.\u003C/li\u003E\n\u003C/ul\u003E\u003Cp\u003EThese limitations underscore the need for an evaluation framework that better reflects the complexities of real-world BI tasks.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"title_v2_2":{"id":"blog-title-f12d24505f","propertiesId":"a-more-realistic-approach-to-evaluation","type":"heading3","lines":["A more realistic approach to evaluation"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_5":{"id":"blog-text-eef2a66d8e","text":"\u003Cp\u003ETo accurately measure the performance of Cortex Analyst, we developed an internal benchmark suite of 150 questions that mirror the real-world tasks business users encounter, covering topics such as sales, marketing, finance, etc. This approach includes:\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"title_v2_3":{"id":"blog-title-db206b58e7","propertiesId":"typical-bi-questions","type":"heading4","lines":["Typical BI questions"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_6":{"id":"blog-text-fd660d088f","text":"\u003Cp\u003EWe categorized questions into three levels — filtering, aggregation and trends — covering a broad spectrum of BI queries. For example:\u003C/p\u003E\u003Cul class=\"wp-block-list\"\u003E\n\u003Cli\u003ELevel 1 questions (filtering): \"Give me the list of the top 10 clients who made the most purchases in the last month.\"\u003C/li\u003E\n\n\n\n\u003Cli\u003ELevel 2 questions (aggregation): \"How much revenue came from book sales in the last two weeks of April 2023?\"\u003C/li\u003E\n\n\n\n\u003Cli\u003ELevel 3 Questions (trends): \"Did any product lines show consecutive revenue increases for five or more days in 2023?”\u003C/li\u003E\n\u003C/ul\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"title_v2_4":{"id":"blog-title-7535492a6d","propertiesId":"schema-shapes","type":"heading4","lines":["Schema shapes"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"title_v2_5":{"id":"blog-title-0d391049a6","propertiesId":"dimension-vs-measures-column-setup","type":"heading5","lines":["Dimension vs. measures column setup"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_7":{"id":"blog-text-df5b11bd12","text":"\u003Cp\u003EOur initial focus was on ensuring high accuracy across a wide variety of customer use cases on a single view with pre-joined data. For this scope, curated BI tables are typically structured with clear distinctions between dimensions, time dimensions and measures. This often results in a flattened, wide table, which is more suitable for data analytics than a long, narrow table.\u003C/p\u003E\u003Cp\u003EFor example, while the following schema shapes are often seen in raw data feeds, they are not ideal for data analytics:\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"markup_editor_1":{"id":"markup-editor-89edf8e75e","title":"Table Information","htmlContent":"\u003Ctable\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cstrong\u003EDate\u003C/strong\u003E\u003C/td\u003E\u003Ctd\u003E\u003Cstrong\u003EMetric Name\u003C/strong\u003E\u003C/td\u003E\u003Ctd\u003E\u003Cstrong\u003EMetric Value\u003C/strong\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E2024/01/01\u003C/td\u003E\u003Ctd\u003ESales\u003C/td\u003E\u003Ctd\u003E1,000,000\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E2024/01/01\u003C/td\u003E\u003Ctd\u003ECost\u003C/td\u003E\u003Ctd\u003E500,000\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E2024/01/01\u003C/td\u003E\u003Ctd\u003ERevenue\u003C/td\u003E\u003Ctd\u003E500,000\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false},"blog_text_8":{"id":"blog-text-374399d02b","text":"\u003Cstrong\u003ETable 1.\u003C/strong\u003E An example of table shape not frequently used in BI.\u003Cp\u003E\u003C/p\u003E\u003Cp\u003EAs we expand to support joins and more complex schemas, we are focusing on the most popular data modeling approach: dimensional data modeling. This approach organizes data into facts and dimensions, typically using a star schema or snowflake schema setup, which aligns more closely with best practices in business intelligence.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"title_v2_6":{"id":"blog-title-64c76ec68b","propertiesId":"time-series-data-considerations","type":"heading5","lines":["Time-series data considerations"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_9":{"id":"blog-text-c8f89c7041","text":"\u003Cp\u003EAs previously mentioned, time-series data, like sales or user activity, is often underrepresented in traditional benchmarks. In real-world scenarios, we frequently encounter cases where the time axis is nonconsecutive, which requires a different approach in SQL to handle calculations correctly.\u003C/p\u003E\u003Cp\u003EOne of the challenges with a nonconsecutive time axis is that it can lead to errors in analysis, particularly in \"month-over-month\" or \"week-over-week\" comparisons. Two common SQL approaches to address this — using self-joins or window functions like LEAD and LAG — can produce seemingly correct results, but they might be misleading if the underlying time data isn’t consecutive.\u003C/p\u003E\u003Cp\u003EFor example:\u003C/p\u003E\u003Cul class=\"wp-block-list\"\u003E\n\u003Cli\u003EA self-join explicitly matches the current period with the prior period. This method is generally more robust, especially when handling nonconsecutive time data.\u003C/li\u003E\n\n\n\n\u003Cli\u003ELEAD/LAG functions rely on the ordering of the time axis and may fail to compare the correct periods if the data isn’t consecutive.\u003C/li\u003E\n\u003C/ul\u003E\u003Cp\u003EWithout careful validation and intermediate calculations (like showing both current and previous periods), there’s a risk that end users could make decisions based on inaccurate comparisons.\u003C/p\u003E\u003Cp\u003EIn our evaluation data set and within Cortex Analyst, we’ve accounted for these nuances. We ensure the system favors more reliable methods and provides sufficient intermediate steps for users to validate the results themselves.\u003C/p\u003E\u003Cp\u003ESee details in the later section, “Evaluation result examples: Business Question 2,” on how we handle “day-over-day” type questions differently, considering possible nonconsecutive time axis.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"title_v2_7":{"id":"blog-title-dd36ef6fa2","propertiesId":"sql-distributions","type":"heading4","lines":["SQL distributions"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_10":{"id":"blog-text-2e893eedd9","text":"\u003Cp\u003ETo flexibly answer a variety of business questions across different schema shapes, it naturally requires our system to generate SQL beyond simple “SELECT… FROM… WHERE” statements. While SQL distribution naturally follows from covering typical BI questions and schema shapes, we also defined key SQL element distribution to ensure our system covers a wide range of SQL capabilities, such as:\u003C/p\u003E\u003Cul class=\"wp-block-list\"\u003E\n\u003Cli\u003EAggregation\n\u003Cul class=\"wp-block-list\"\u003E\n\u003Cli\u003EBasic aggregations (SUM, COUNT, MIN, MAX, AVG)\u003C/li\u003E\n\n\n\n\u003Cli\u003EAdvanced aggregations (CORR, PERCENT_RANK, . . .)\u003C/li\u003E\n\u003C/ul\u003E\n\u003C/li\u003E\n\n\n\n\u003Cli\u003ECTE, Subqueries\u003C/li\u003E\n\n\n\n\u003Cli\u003EWindow functions\u003C/li\u003E\n\n\n\n\u003Cli\u003EJoin\n\u003Cul class=\"wp-block-list\"\u003E\n\u003Cli\u003ECurrently, as we focus on single view with pre-joined data, this primarily involves self-joins. As we expand our scope to support cross-table joins, we’ll start off my supporting star and snowflake schemas, and eventually expand to joins in arbitrary schema shapes.\u003C/li\u003E\n\u003C/ul\u003E\n\u003C/li\u003E\n\u003C/ul\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_3":{"id":"image-df6aadc5d5","alt":"Figure 3. Distribution of features in our internal evaluation set","lazyEnabled":true,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--433e487e-9810-40f1-85f2-e03842d11d0d/figure4-large.jpg?quality=85&preferwebp=true","height":"764","width":"1280","title":"Figure 3. Distribution of features in our internal evaluation set",":type":"snowflake-site/components/image"},"title_v2_8":{"id":"blog-title-0b6f7431db","propertiesId":"snowflake-cortex-analyst-product-feature-evaluation","type":"heading3","lines":["Snowflake Cortex Analyst product feature evaluation"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_11":{"id":"blog-text-6b5765ce90","text":"\u003Cp\u003ETo ensure high accuracy and reliability of SQL generation, Cortex Analyst incorporates several key features that enhance its performance in real-world business intelligence tasks. It’s critical to ensure the reliability of those features. \u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"title_v2_9":{"id":"blog-title-c7b9eba18b","propertiesId":"reliable-use-of-defined-measures-and-filters","type":"heading4","lines":["Reliable use of defined measures and filters"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_12":{"id":"blog-text-c4f464c162","text":"\u003Cp\u003EOne of the standout features of Cortex Analyst is its semantic model, which guarantees that user-defined measures and filters are applied highly consistently in the generated SQL queries. This feature is crucial for maintaining accuracy across different use cases.\u003C/p\u003E\u003Cp\u003EFor example, in the benchmark semantic model, we define named filters to capture enterprise-specific jargon. For example, an enterprise might define a filter “North America” as following:\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_1":{"id":"code-snippet-5dcd0e0a0a","language":"sql","codeSnippet":"filters:\r\n- name: North America\r\nsynonyms:\r\n- NA region\r\nexpr: region in (\"United States\", \"Canada\", \"Mexico\")\r\n","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_13":{"id":"blog-text-7cd5f2aec4","text":"\u003Cp\u003EThen, for questions like, “What’s my total revenue in North America in 2023?”, the SQL generated by Cortex Analyst is considered correct only if it applies these definitions precisely.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_2":{"id":"code-snippet-59175f9fc6","language":"sql","codeSnippet":"SELECT sum(revenue)\r\nFROM daily_revenue_by_region\r\nWHERE region in (\"United States\", \"Canada\", \"Mexico\")\r\nand year(dt) = '2023'","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_14":{"id":"blog-text-e2f3cc0f24","text":"\u003Cp\u003EThis approach ensures that the SQL output aligns perfectly with the predefined business rules, providing users with trustworthy results.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"title_v2_10":{"id":"blog-title-2b8bed7543","propertiesId":"literal-retrieval","type":"heading4","lines":["Literal retrieval"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_15":{"id":"blog-text-c49605e5d0","text":"\u003Cp\u003ELiteral matching is another critical feature in Cortex Analyst that contributes to SQL accuracy. Users can include specific sample values within their queries, and the system ensures that these literals are correctly mapped and retrieved in the SQL output. \u003C/p\u003E\u003Cp\u003EFor columns with high cardinality, it’s unrealistic to include all distinct values as sample values. To address this, we introduced integration with Cortex Search, which allows Cortex Analyst to retrieve the correct literals for SQL generation by performing a semantic search over the data stored in those columns. This functionality is currently in private preview.\u003C/p\u003E\u003Cp\u003EIn our evaluation, we included questions that require exact literal matching, and the generated SQL was only considered accurate when it precisely matched these defined values.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"title_v2_11":{"id":"blog-title-b7c841d164","propertiesId":"evaluation-metrics-and-methodology","type":"heading3","lines":["Evaluation metrics and methodology"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"title_v2_12":{"id":"blog-title-242d42514a","propertiesId":"the-challenge-one-question-can-have-multiple-correct-answers","type":"heading4","lines":["The Challenge: One question can have multiple correct answers"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_16":{"id":"blog-text-11a8968f61","text":"\u003Cp\u003EIn text-to-SQL, one might assume that evaluating metrics is straightforward — just execute the gold query and compare it to the generated query for an exact match. This method, known as \"execution accuracy,\" is widely used in popular benchmarks.\u003Csup\u003E1\u003C/sup\u003E\u003C/p\u003E\u003Cp\u003EHowever, this approach has significant limitations. It often rejects perfectly valid SQL queries simply because their execution results don't match the gold query exactly. In reality, many business questions can be answered in multiple valid ways.\u003C/p\u003E\u003Cp\u003EFor instance, consider the question: \"Do we sell twice as many toys as books?\"\u003C/p\u003E\u003Cp\u003EOne SQL query might return a simple \"YES\" or \"NO\" based on the revenue ratio. Another might output the exact revenues for both toys and books, along with the calculated ratio, allowing the end user to validate the result themselves. Both SQL queries would generate different outputs but answer the question correctly. In fact, many users might prefer the detailed output, as it provides more context and validation.\u003C/p\u003E\u003Cp\u003E Example 1: Simple YES/NO Output\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_3":{"id":"code-snippet-0eae7554ad","language":"sql","codeSnippet":"with toys_revenue as (\r\nselect sum(revenue) as total_toys_revenue\r\nfrom daily_revenue where product = 'Toys'\r\n),\r\nbooks_revenue as (\r\nselect sum(revenue) as total_books_revenue\r\nfrom daily_revenue where product = 'Books'\r\n)\r\nSELECT\r\nCASE\r\nWHEN total_toys_revenue \u003E= 2 * total_books_revenue THEN 'Yes'\r\nELSE 'No'\r\nEND AS is_toys_twice_books\r\nFROM\r\ntoys_revenue,\r\nbooks_revenue","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_17":{"id":"blog-text-db9480e8a7","text":"\u003Cp\u003EExample 2: Outputting Revenues and Ratio\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_4":{"id":"code-snippet-bae049f3ce","language":"sql","codeSnippet":"with toys_revenue as (\r\nselect sum(revenue) as total_toys_revenue\r\nfrom daily_revenue where product = 'Toys'\r\n),\r\nbooks_revenue as (\r\nselect sum(revenue) as total_books_revenue\r\nfrom daily_revenue where product = 'Books'\r\n)\r\nSELECT\r\ntotal_toys_revenue,\r\ntotal_books_revenue,\r\ndiv0(total_toys_revenue, total_books_revenue) as ratio_toy_to_book\r\nFROM\r\ntoys_revenue,\r\nbooks_revenue","multiLine":true,":type":"snowflake-site/components/code-snippet"},"title_v2_13":{"id":"blog-title-44854595f9","propertiesId":"snowflake-evaluation-approach-scoring-with-multiple-gold-queries","type":"heading4","lines":["Snowflake evaluation approach: Scoring with multiple gold queries"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_18":{"id":"blog-text-4667edcf8a","text":"\u003Cp\u003ETo address these challenges, we experimented with several methods for evaluating SQL more flexibly, including using large language models (LLMs) as judges. However, we found that evaluating SQL can be as challenging as generating the correct SQL itself.\u003C/p\u003E\u003Cp\u003EGiven the limitations of strict execution accuracy and the challenges of using LLMs as judges, our team adopted a more iterative approach:\u003C/p\u003E\u003Col class=\"wp-block-list\"\u003E\n\u003Cli\u003E\u003Cstrong\u003EDiverse query generation: \u003C/strong\u003EWe run evaluation questions across multiple language models to generate a broad search space of possible SQL queries.\u003C/li\u003E\n\n\n\n\u003Cli\u003E\u003Cstrong\u003EHuman-informed gold standards:\u003C/strong\u003E From this search space, human evaluators manually select all the correct SQL queries to add to our set of acceptable gold queries.\u003C/li\u003E\n\n\n\n\u003Cli\u003E\u003Cstrong\u003EFlexible scoring\u003C/strong\u003E: Instead of requiring an exact match with the gold query, we calculate precision and recall for column matching, setting a more lenient threshold to judge accuracy. This approach focuses on meaningful matches rather than strict exactness.\u003C/li\u003E\n\u003C/ol\u003E\u003Cp\u003EAs Cortex Analyst aims to address more diverse customer use cases, the risks associated with using a single-answer evaluation grow significantly, as it can lead to biased insights during model error analysis. By allowing for multiple correct answers, our evaluation methodology better reflects the nuances of real-world data analysis, providing a more accurate assessment of Cortex Analyst's capabilities. \u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"title_v2_14":{"id":"blog-title-e8e2857763","propertiesId":"evaluation-result-examples","type":"heading4","lines":["Evaluation result examples"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_19":{"id":"blog-text-4671dcce80","text":"\u003Cp\u003EHere are some examples of evaluation results where Cortex Analyst outperforms the GPT-4o single-shot solution and the Other Solution we benchmarked against.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"markup_editor_2":{"id":"markup-editor-d9b3be02cf","title":"Table Information","htmlContent":"\u003Ctable\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003EBusiness Question\u003C/td\u003E\u003Ctd\u003ECortex Analyst\u003C/td\u003E\u003Ctd\u003EGPT-4o Single shot\u003C/td\u003E\u003Ctd\u003EOther Solution\u003C/td\u003E\u003Ctd\u003ESummary\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EQ1.Which region sold most on Christmas Day?\u003C/td\u003E\u003Ctd\u003ESucceeds\u003C/td\u003E\u003Ctd\u003EFails\u003C/td\u003E\u003Ctd\u003ESucceeds\u003C/td\u003E\u003Ctd\u003EGPT-4o making assumptions of looking into Christmas Day for a random year. Cortex Analyst and Other Solution looks for the most recent year.\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EQ2.What was the DoD change in COGS during the 2nd week of May 2023, only looking at the North America region?\u003C/td\u003E\u003Ctd\u003ESucceeds\u003C/td\u003E\u003Ctd\u003EFails\u003C/td\u003E\u003Ctd\u003EFails\u003C/td\u003E\u003Ctd\u003EGPT-4o and Other Solution’s generated queries are incorrect if the \u003Cstrong\u003EDATE\u003C/strong\u003E column is nonconsecutive and has missing days.Cortex Analyst-generated query is correct regardless of DATE consecutiveness. \u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EQ3.Show me the 3 day rolling maximum for forecasted revenue from electronics during the 4 weeks leading up to Christmas 2023.\u003C/td\u003E\u003Ctd\u003ESucceeds\u003C/td\u003E\u003Ctd\u003EFails\u003C/td\u003E\u003Ctd\u003EFails\u003C/td\u003E\u003Ctd\u003EGPT-4o and Other Solution’s generated queries filtered on time range before calculating rolling window metric, causing incorrect numbers on the first 3 days of the time window.\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EQ4.Rank the product lines in terms of forecast accuracy.\u003C/td\u003E\u003Ctd\u003ESucceeds\u003C/td\u003E\u003Ctd\u003EFails\u003C/td\u003E\u003Ctd\u003EFails\u003C/td\u003E\u003Ctd\u003EGPT-4o and Other Solution were missing the client's specific metric definition.Cortex Analyst captures the specific metric definition with the semantic model.\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EQ5. For each week, what was the lowest daily cost of goods sold. show the week start date as well as the date that the low value occurred.\u003C/td\u003E\u003Ctd\u003ESucceeds\u003C/td\u003E\u003Ctd\u003ESucceeds\u003C/td\u003E\u003Ctd\u003EFails\u003C/td\u003E\u003Ctd\u003EOther Solution doesn’t follow the prompt and shows every day in the week with the min value repeated, making it impossible to know which day the min value occurred.\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false},"title_v2_15":{"id":"blog-title-e8b215e906","propertiesId":"business-question-1-which-region-sold-most-on-christmas-day","type":"heading5","lines":["Business Question 1: Which region sold most on Christmas Day?"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_20":{"id":"blog-text-9a00b7dfb0","text":"\u003Cp\u003E\u003Cstrong\u003EOutcome:\u003C/strong\u003E Cortex Analyst and the Other Solution succeed by correctly identifying the most recent Christmas day, while GPT-4o fails by making an incorrect assumption.\u003C/p\u003E\u003Cp\u003EThe GPT-4o single-shot solution generated a query that picks Christmas Day in 2022, which could mislead users, as they are most likely interested in the most recent Christmas Day when not specified. Cortex Analyst, on the other hand, is designed to identify the most recent Christmas Day — aligning with probable user intent — and notes this assumption in the rewritten question.\u003C/p\u003E\u003Cp\u003E\u003Cstrong\u003ECortex Analyst Answer:\u003C/strong\u003E\u003C/p\u003E\u003Cp\u003EHere’s our interpretation of your question: “Which region had the most revenue on Christmas Day in 2023?”\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_5":{"id":"code-snippet-15be5c2bc9","language":"sql","codeSnippet":"SELECT\r\nsales_region,\r\nSUM(revenue) AS total_sales\r\nFROM daily_revenue_by_region\r\nWHERE\r\ndate = '2023-12-25'\r\nGROUP BY\r\nsales_region\r\nORDER BY\r\ntotal_sales DESC NULLS LAST\r\nLIMIT 1;","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_21":{"id":"blog-text-59a38248b8","text":"\u003Cp\u003E\u003Cstrong\u003ESingle-prompt GPT-4o Answer:\u003C/strong\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_6":{"id":"code-snippet-385786d80c","language":"sql","codeSnippet":"SELECT\r\nSALES_REGION,\r\nSUM(REVENUE) AS TOTAL_REVENUE\r\nFROM DAILY_REVENUE_BY_REGION\r\nWHERE DATE = '2022-12-25'\r\nGROUP BY SALES_REGION ORDER BY TOTAL_REVENUE DESC LIMIT 1;","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_22":{"id":"blog-text-32d9aff49e","text":"\u003Cp\u003E\u003Cstrong\u003EOther Solution Answer:\u003C/strong\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_7":{"id":"code-snippet-db97e2008a","language":"sql","codeSnippet":"SELECT\r\nSALES_REGION,\r\nSUM(REVENUE) AS total_revenue\r\nFROM\r\ndaily_revenue_by_region\r\nWHERE\r\nDATE = '2023-12-25'\r\nGROUP BY\r\nSALES_REGION\r\nORDER BY\r\ntotal_revenue DESC\r\nLIMIT 1;","multiLine":true,":type":"snowflake-site/components/code-snippet"},"title_v2_16":{"id":"blog-title-f2f1336961","propertiesId":"business-question-2-what-was-the-dod-change-in-cogs-during-the-2nd-week-of-may-2023-only-looking-at-the-north-america-region","type":"heading5","lines":["Business Question 2: What was the DoD change in COGS during the 2nd week of May 2023, only looking at the North America region?"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_23":{"id":"blog-text-fa54b813a2","text":"\u003Cp\u003E\u003Cstrong\u003EOutcome:\u003C/strong\u003E Cortex Analyst provides a correct solution regardless of nonconsecutive dates, while GPT-4o and Other Solution fail by producing a query that gives incorrect answers on nonconsecutive dates.\u003C/p\u003E\u003Cp\u003EAt first glance, the SQL generated by Cortex Analyst, GPT-4o and the Other Solution may all appear reasonable. However, if the “Date” column in the underlying data isn’t consecutive and has missing days, GPT-4o and the Other Solution’s use of the LAG function would be incorrect, as it doesn’t ensure comparison with the actual prior date.\u003C/p\u003E\u003Cp\u003EThis issue becomes even more problematic if the SQL only outputs the current date, without the previous date column. In such cases, the end user cannot verify if the calculation is correctly compared against the previous date, and could easily make decisions based on completely incorrect numbers.\u003C/p\u003E\u003Cp\u003ECortex Analyst ensures accurate calculations even with nonconsecutive dates by validating comparisons against the correct prior date.\u003C/p\u003E\u003Cp\u003E\u003Cstrong\u003ECortex Analyst Answer:\u003C/strong\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_8":{"id":"code-snippet-81abe2ebec","language":"sql","codeSnippet":"WITH daily_cogs AS (\r\nSELECT\r\nsales_region,\r\ndate,\r\ncogs\r\nFROM daily_revenue_by_region\r\nWHERE\r\nsales_region = 'North America' AND date \u003E= '2023-05-08' AND date \u003C '2023-05-15'\r\n)\r\nSELECT\r\ncurr.date AS curr_date,\r\nprev.date AS prev_date,\r\ncurr.cogs AS curr_cogs,\r\nprev.cogs AS prev_cogs,\r\ncurr_cogs - prev_cogs AS dod_chg,\r\n(\r\ncurr_cogs - prev_cogs\r\n) / NULLIF(prev_cogs, 0) AS dod_pct_chg\r\nFROM daily_cogs AS curr\r\nLEFT JOIN daily_cogs AS prev\r\nON (\r\ncurr.date = prev.date + INTERVAL '1 DAY'\r\n)\r\nORDER BY\r\ncurr_date DESC NULLS LAST","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_24":{"id":"blog-text-4d70d0b5a3","text":"\u003Cp\u003E\u003Cstrong\u003ESingle-prompt GPT-4o Answer:\u003C/strong\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_9":{"id":"code-snippet-34ca54de63","language":"sql","codeSnippet":"WITH cte AS (\r\nSELECT\r\nDATE, COGS,\r\nLAG(COGS) OVER (ORDER BY DATE) AS PREV_COGS\r\nFROM DAILY_REVENUE_BY_REGION\r\nWHERE SALES_REGION = 'North America' AND DATE BETWEEN '2023-05-08' AND '2023-05-14'\r\n)\r\nSELECT\r\nDATE, COGS, PREV_COGS,\r\n(COGS - PREV_COGS) / NULLIF(PREV_COGS, 0) * 100 AS DOD_CHANGE_PERCENT\r\nFROM cte\r\nWHERE NOT PREV_COGS IS NULL ORDER BY DATE;","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_25":{"id":"blog-text-63ebe06cd9","text":"\u003Cp\u003E\u003Cstrong\u003EOther Solution Answer:\u003C/strong\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_10":{"id":"code-snippet-bb8a802c18","language":"sql","codeSnippet":"WITH CogsCalc AS (\r\nSELECT\r\nDATE,\r\nCOGS,\r\nLAG(COGS) OVER (\r\nORDER BY\r\nDATE\r\n) AS previous_day_cogs,\r\nCOGS - LAG(COGS) OVER (\r\nORDER BY\r\nDATE\r\n) AS cogs_change\r\nFROM\r\ndaily_revenue_by_region\r\nWHERE\r\nSALES_REGION ILIKE '%North America%'\r\nAND DATE \u003E= '2023-05-08'\r\nAND DATE \u003C= '2023-05-14'\r\n)\r\nSELECT\r\nDATE,\r\ncogs_change\r\nFROM\r\nCogsCalc\r\nORDER BY\r\nDATE","multiLine":true,":type":"snowflake-site/components/code-snippet"},"title_v2_17":{"id":"blog-title-6efaef116d","propertiesId":"business-question-3-show-me-the-3-day-rolling-maximum-for-forecasted-revenue-from-electronics-during-the-4-weeks-leading-up-to-christmas-2023","type":"heading5","lines":["Business Question 3: Show me the 3 day rolling maximum for forecasted revenue from electronics during the 4 weeks leading up to Christmas 2023."],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_26":{"id":"blog-text-30dfbecfee","text":"\u003Cp\u003E\u003Cstrong\u003EOutcome: \u003C/strong\u003ECortex Analyst succeeds by calculating the rolling maximum before filtering the date range, while GPT-4o and the Other Solution fail by incorrectly applying the date filter first.\u003C/p\u003E\u003Cp\u003EThis shows another example of very similar-looking calculations giving different results. For \u003Cem\u003Erolling window\u003C/em\u003E questions, the sequence of operations — whether filtering the date range before or after applying the rolling window function — is crucial.  \u003C/p\u003E\u003Cp\u003EIn this example, GPT-4o incorrectly filters the date range first, leading to incorrect values for the first three days of the window. Cortex Analyst, however, performs the rolling calculation first and then applies the date filter, retrieving accurate results. \u003C/p\u003E\u003Cp\u003E\u003Cstrong\u003ECortex Analyst Answer (Calculate rolling max first, then filter to desired range)\u003C/strong\u003E:\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_11":{"id":"code-snippet-a8165d0d49","language":"sql","codeSnippet":"WITH rolling_max AS (\r\nSELECT\r\ndate,\r\nforecasted_revenue,\r\nMAX(forecasted_revenue) OVER (ORDER BY date ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) AS rolling_max_3d\r\nFROM daily_revenue_by_product\r\nWHERE\r\nproduct_line = 'Electronics'\r\n)\r\nSELECT\r\ndate,\r\nrolling_max_3d\r\nFROM rolling_max\r\nWHERE\r\ndate BETWEEN CAST('2023-12-25' AS DATE) - INTERVAL '4 WEEKS' AND CAST('2023-12-25' AS DATE)\r\nORDER BY\r\ndate DESC NULLS LAST;","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_27":{"id":"blog-text-b50a6ef3b1","text":"\u003Cp\u003E\u003Cstrong\u003ESingle-prompt GPT-4o Answer (Filter to date range first, then rolling max)\u003C/strong\u003E:\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_12":{"id":"code-snippet-82a08b7a2b","language":"sql","codeSnippet":"WITH FilteredData AS (\r\nSELECT\r\nDATE, FORECASTED_REVENUE\r\nFROM SNOWPILOT_SYNTHETIC_DATA.REVENUE_TIMESERIES.DAILY_REVENUE_BY_PRODUCT\r\nWHERE PRODUCT_LINE = 'Electronics' AND DATE BETWEEN '2023-11-27' AND '2023-12-24'\r\n), RollingMax AS (\r\nSELECT\r\nDATE, FORECASTED_REVENUE,\r\nMAX(FORECASTED_REVENUE) OVER (ORDER BY DATE ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) AS ROLLING_MAX_FORECASTED_REVENUE\r\nFROM FilteredData\r\n)\r\nSELECT DATE, ROLLING_MAX_FORECASTED_REVENUE FROM RollingMax ORDER BY DATE;","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_28":{"id":"blog-text-4d7e2468dc","text":"\u003Cp\u003E\u003Cstrong\u003EOther Solution Answer\u003C/strong\u003E:\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_13":{"id":"code-snippet-9a7719c3b3","language":"sql","codeSnippet":"WITH DateRange AS (\r\nSELECT\r\nDATE_SUB('2023-12-25', 27) AS start_date,\r\n'2023-12-25' AS end_date\r\n),\r\nRollingMax AS (\r\nSELECT\r\nDATE,\r\nMAX(FORECASTED_REVENUE) OVER (\r\nORDER BY\r\nDATE ROWS BETWEEN 2 PRECEDING\r\nAND CURRENT ROW\r\n) AS rolling_max_forecasted_revenue\r\nFROM\r\ndaily_revenue_by_product,\r\nDateRange\r\nWHERE\r\nDATE BETWEEN start_date\r\nAND end_date\r\nAND PRODUCT_LINE ILIKE '%Electronics%'\r\n)\r\nSELECT\r\nDATE,\r\nrolling_max_forecasted_revenue\r\nFROM\r\nRollingMax\r\nORDER BY\r\nDATE;","multiLine":true,":type":"snowflake-site/components/code-snippet"},"title_v2_18":{"id":"blog-title-54fa11ea75","propertiesId":"business-question-4-rank-the-product-lines-in-terms-of-forecast-accuracy","type":"heading5","lines":["Business Question 4: Rank the product lines in terms of forecast accuracy."],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_29":{"id":"blog-text-b93a20864e","text":"\u003Cp\u003E\u003Cstrong\u003EOutcome: \u003C/strong\u003ECortex Analyst succeeds by aligning with the user's specific metric definition, while GPT-4o and the Other Solution fail by making incorrect assumptions about the metric.\u003C/p\u003E\u003Cp\u003EWhen it comes to ranking product lines based on forecast accuracy, the definition of \"accuracy\" can vary. In this case, Cortex Analyst uses a user-defined semantic model to correctly calculate accuracy based on absolute differences, as specified. GPT-4o and the Other Solution, however, attempt to infer the metric on their own, resulting in a calculation based on percentage differences, which may not align with user expectations.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_14":{"id":"code-snippet-0eee75bdf9","language":"sql","codeSnippet":"measures:\r\n- name: avg_forecast_error\r\nexpr: AVG(ABS(forecasted_revenue - revenue))\r\nsynonyms: ['forecast accuracy']\r\ndata_type: number","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_30":{"id":"blog-text-3bc34ecc85","text":"\u003Cp\u003E\u003Cstrong\u003ECortex Analyst Answer\u003C/strong\u003E:\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_15":{"id":"code-snippet-fde9a250ae","language":"sql","codeSnippet":"WITH accuracy as (\r\nSELECT\r\nproduct_line,\r\nAVG(ABS(forecasted_revenue - revenue)) AS avg_forecast_error\r\nFROM daily_revenue_by_product)\r\nGROUP BY\r\nproduct_line\r\n)\r\nselect product_line,\r\navg_forecast_error,\r\nrank() over (order by avg_forecast_error asc) as rnk\r\nfrom accuracy\r\nORDER BY\r\navg_forecast_error ASC;","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_31":{"id":"blog-text-cf4673b1e5","text":"\u003Cp\u003E\u003Cstrong\u003ESingle-prompt GPT-4o Answer\u003C/strong\u003E:\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_16":{"id":"code-snippet-127aef34f2","language":"sql","codeSnippet":"WITH forecast_accuracy AS (\r\nSELECT\r\nPRODUCT_LINE,\r\nAVG(ABS(FORECASTED_REVENUE - REVENUE) / NULLIF(REVENUE, 0)) AS MAPE\r\nFROM SNOWPILOT_SYNTHETIC_DATA.REVENUE_TIMESERIES.DAILY_REVENUE_BY_PRODUCT\r\nGROUP BY PRODUCT_LINE\r\n)\r\nSELECT PRODUCT_LINE, MAPE, RANK() OVER (ORDER BY MAPE ASC) AS ACCURACY_RANK\r\nFROM forecast_accuracy ORDER BY ACCURACY_RANK;","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_32":{"id":"blog-text-4ac0fe1bb6","text":"\u003Cp\u003E\u003Cstrong\u003EOther Solution Answer\u003C/strong\u003E:\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_17":{"id":"code-snippet-968e776f4a","language":"sql","codeSnippet":"SELECT\r\nPRODUCT_LINE,\r\nAVG(ABS(FORECASTED_REVENUE - REVENUE) / REVENUE) AS forecast_accuracy\r\nFROM\r\ndaily_revenue_by_product\r\nGROUP BY\r\nPRODUCT_LINE\r\nORDER BY\r\nforecast_accuracy;","multiLine":true,":type":"snowflake-site/components/code-snippet"},"title_v2_19":{"id":"blog-title-49aec3e28d","propertiesId":"business-question-5-for-each-week-what-was-the-lowest-daily-cost-of-goods-sold-show-the-week-start-date-as-well-as-the-date-that-the-low-value-occurred","type":"heading5","lines":["Business Question 5: For each week, what was the lowest daily cost of goods sold. Show the week start date, as well as the date that the low value occurred."],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_33":{"id":"blog-text-6d72e41e51","text":"\u003Cp\u003E\u003Cstrong\u003EOutcome:\u003C/strong\u003E Cortex Analyst and GPT-4o succeed by finding min COGS date for each week and the corresponding date when the min COGS value occurred, whereas the Other Solution fails because it calculates the min COGS value for each week but does not identify which date within the week it occurred.\u003C/p\u003E\u003Cp\u003E\u003Cstrong\u003ECortex Analyst Answer\u003C/strong\u003E:\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_18":{"id":"code-snippet-7906c70e73","language":"sql","codeSnippet":"WITH weekly_min_cogs AS (\r\nSELECT\r\nDATE_TRUNC('WEEK', date) AS week_start_date,\r\nMIN(cogs) AS min_cogs\r\nFROM snowpilot_synthetic_data.revenue_timeseries.daily_revenue\r\nGROUP BY\r\nDATE_TRUNC('WEEK', date)\r\n)\r\nSELECT\r\nwmc.week_start_date,\r\nwmc.min_cogs,\r\ndr.date AS min_cogs_date\r\nFROM weekly_min_cogs AS wmc\r\nJOIN snowpilot_synthetic_data.revenue_timeseries.daily_revenue AS dr\r\nON wmc.week_start_date = DATE_TRUNC('WEEK', dr.date)\r\nAND wmc.min_cogs = dr.cogs\r\nORDER BY\r\nwmc.week_start_date DESC NULLS LAST;","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_34":{"id":"blog-text-06adcfb812","text":"\u003Cp\u003E\u003Cstrong\u003ESingle-prompt GPT-4o Answer\u003C/strong\u003E:\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_19":{"id":"code-snippet-94a384aafb","language":"sql","codeSnippet":"WITH WEEKLY_COGS AS (\r\nSELECT\r\nDATE_TRUNC('WEEK', DATE) AS WEEK_START_DATE,\r\nDATE,\r\nCOGS,\r\nROW_NUMBER() OVER (\r\nPARTITION BY DATE_TRUNC('WEEK', DATE)\r\nORDER BY\r\nCOGS ASC\r\n) AS RN\r\nFROM\r\nSNOWPILOT_SYNTHETIC_DATA.REVENUE_TIMESERIES.DAILY_REVENUE\r\n)\r\nSELECT\r\nWEEK_START_DATE,\r\nDATE AS LOW_COGS_DATE,\r\nCOGS AS LOW_COGS\r\nFROM\r\nWEEKLY_COGS\r\nWHERE\r\nRN = 1\r\nORDER BY\r\nWEEK_START_DATE;","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_35":{"id":"blog-text-7102940799","text":"\u003Cp\u003E\u003Cstrong\u003EOther Solution Answer\u003C/strong\u003E:\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_20":{"id":"code-snippet-ed1772c2bd","language":"sql","codeSnippet":"WITH WeeklyCOGS AS (\r\nSELECT\r\nDATE_TRUNC('week', DATE) AS week_start_date,\r\nDATE,\r\nMIN(COGS) OVER (PARTITION BY DATE_TRUNC('week', DATE)) AS weekly_min_cogs\r\nFROM\r\ndaily_revenue\r\n)\r\nSELECT\r\nDISTINCT week_start_date,\r\nDATE AS low_cogs_date,\r\nweekly_min_cogs\r\nFROM\r\nWeeklyCOGS\r\nORDER BY\r\nweek_start_date,\r\nlow_cogs_date;","multiLine":true,":type":"snowflake-site/components/code-snippet"},"title_v2_20":{"id":"blog-title-d3af6fa204","propertiesId":"conclusion","type":"heading3","lines":["Conclusion"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_36":{"id":"blog-text-623d107d76","text":"\u003Cp\u003ETo summarize, in this blog we discussed our approach to better reflect real-world text-to-SQL challenges, both by constructing an internal evaluation benchmark and incorporating multiple gold queries to enhance the evaluation methodology. Additionally, the Spider team has announced that Spider 2.0-SQL is coming out soon and will be much more realistic and challenging than Spider 1.0. We’ll aim to evaluate our system on the Spider 2.0 data set when it’s available.\u003C/p\u003E\u003Cp\u003EInterested in trying out Snowflake Cortex Analyst?  Build your first Cortex Analyst-powered chat app using this \u003Ca href=\"https://quickstarts.snowflake.com/guide/getting_started_with_cortex_analyst/index.html\" target=\"_blank\" rel=\"noreferrer noopener\"\u003E\u003Cstrong\u003Equickstart guide\u003C/strong\u003E\u003C/a\u003E today!\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_37":{"id":"blog-text-4dea51ab5e","text":"\u003Cp\u003E\u003Csup\u003E1\u003C/sup\u003E See \u003Ca href=\"https://aclanthology.org/D18-1425.pdf\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"\u003Eoriginal Spider paper\u003C/a\u003E on evaluation metric.\u003C/p\u003E\u003Cp\u003E\u003Cstrong\u003EForward-Looking Statements\u003C/strong\u003E\u003C/p\u003E\u003Cp\u003EThis contains express and implied forward-looking statements, including statements regarding (i) Snowflake’s business strategy, (ii) Snowflake’s products, services, and technology offerings, including those that are under development or not generally available, (iii) market growth, trends, and competitive considerations, and (iv) the integration, interoperability, and availability of Snowflake’s products with and on third-party platforms. These forward-looking statements are subject to a number of risks, uncertainties, and assumptions, including those described under the heading “Risk Factors” and elsewhere in the Quarterly Reports on Form 10-Q and Annual Reports of Form 10-K that Snowflake files with the Securities and Exchange Commission. In light of these risks, uncertainties, and assumptions, actual results could differ materially and adversely from those anticipated or implied in the forward-looking statements. As a result, you should not rely on any forward-looking statements as predictions of future 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