{"id":35568,"date":"2026-03-19T16:52:04","date_gmt":"2026-03-19T15:52:04","guid":{"rendered":"https:\/\/www.codemotion.com\/magazine\/?p=35568"},"modified":"2026-03-19T16:52:06","modified_gmt":"2026-03-19T15:52:06","slug":"snowflake-beyond-sql-generation-teaching-ai-agents-what-your-database-really-means","status":"publish","type":"post","link":"https:\/\/www.codemotion.com\/magazine\/ai-ml\/snowflake-beyond-sql-generation-teaching-ai-agents-what-your-database-really-means\/","title":{"rendered":"Snowflake \u2013 \u201cBeyond SQL Generation\u201d: Teaching AI Agents What Your Database Really Means"},"content":{"rendered":"\n

For decades, SQL has been the universal language organizations use to query their data. Simple in its basic syntax yet powerful in its expressiveness, SQL has survived every technological wave\u2014from the relational databases of the 1980s to modern data warehouses\u2014remaining the fundamental tool developers, analysts, and systems of all kinds use to access structured information.<\/p>\n\n\n\n

It\u2019s no surprise, then, that when generative AI began transforming software development, SQL became one of the first areas of experimentation. The promise was compelling: AI agents capable of answering questions in natural language by automatically translating them into SQL queries, eliminating the technical barrier between those who have a question and those who know how to extract the answer from a database.<\/p>\n\n\n\n

The problem no one expected<\/strong><\/h3>\n\n\n\n

In theory, it works. In practice, something goes wrong.<\/p>\n\n\n\n

Modern AI agents\u2014even sophisticated ones like Claude\u2014are actually very good at writing syntactically correct SQL. They can construct complex joins, advanced aggregations, and well-formed subqueries. The problem isn\u2019t grammar. The problem is meaning.<\/p>\n\n\n\n

An agent that doesn\u2019t know your domain doesn\u2019t know that the status<\/strong> column in the orders<\/strong> table follows a specific business logic. It doesn\u2019t know that certain fields are deprecated and shouldn\u2019t be used in current analyses. It doesn\u2019t know that two seemingly similar tables represent radically different concepts in your organization\u2019s context. The result? Queries that are technically flawless but return incorrect answers. And when faced with ambiguity, the agent doesn\u2019t ask for clarification\u2014it guesses.<\/p>\n\n\n\n

This is exactly the problem organizations encounter when they try to build AI analysts on top of real, complex databases. The tool seems to work in demos, then fails in production\u2014not with obvious errors, but with plausible yet incorrect answers, which are the most dangerous kind.<\/p>\n\n\n\n

The solution: give the agent a map of the territory<\/strong><\/h3>\n\n\n\n

The diagnosis, as Kris Jenkins explains, is clear: the agent knows SQL syntax, but it doesn\u2019t know your software. It\u2019s like hiring an expert in Italian grammar and expecting them to understand how your company operates.<\/p>\n\n\n\n

The solution is equally clear: you need to teach it the domain. Not through improvised prompts or long informal instructions, but through a structured tool\u2014the semantic model<\/strong>. A file that encodes all the tacit knowledge an experienced new hire would accumulate during their first six months: how tables relate to each other, what column names really mean, which types of queries make sense, which patterns are correct, and which should be avoided.<\/p>\n\n\n\n

It\u2019s the difference between handing someone a detailed map of the territory and letting them wander around hoping they\u2019ll eventually find the right path.<\/p>\n\n\n\n

In his talk at Codemotion Rome 2026<\/a>, Jenkins\u2014Lead Developer Advocate at Snowflake and host of the Developer Voices<\/em> podcast, with a career that has included being CTO of a gold trading company, a Haskell specialist contractor, and a hackathon organizer\u2014will guide the audience through the practical details of semantic models and the OSI standard. He will explain why standardization in this area is essential for scalability and will demonstrate concrete techniques for building effective semantic models quickly.<\/p>\n\n\n\n

The ultimate goal is both simple and ambitious: an AI-based database analyst that is reliable and effective from day one\u2014not after months of fixes and prompt engineering.<\/p>\n\n\n\n

Come and see it in person.<\/strong>The talk \u201cBeyond SQL Generation: How to Teach Agents What Your Database Means\u201d<\/em> by Kris Jenkins will take place at Codemotion Rome 2026. If you work with AI agents, data, and databases, it\u2019s a session you won\u2019t want to miss.<\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

For decades, SQL has been the universal language organizations use to query their data. Simple in its basic syntax yet powerful in its expressiveness, SQL has survived every technological wave\u2014from the relational databases of the 1980s to modern data warehouses\u2014remaining the fundamental tool developers, analysts, and systems of all kinds use to access structured information.… Read more<\/a><\/p>\n","protected":false},"author":64,"featured_media":33425,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_editorskit_title_hidden":false,"_editorskit_reading_time":0,"_editorskit_is_block_options_detached":false,"_editorskit_block_options_position":"{}","_uag_custom_page_level_css":"","_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"","footnotes":""},"categories":[46],"tags":[12347,3360],"collections":[11387],"class_list":{"0":"post-35568","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-ai-ml","8":"tag-claude","9":"tag-database","10":"collections-top-of-the-week","11":"entry"},"yoast_head":"\nBeyond SQL Generation - 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