[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-text-to-sql::en":3,"gloss-cluster-text-to-sql::en":26,"gloss-next-text-to-sql::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"text-to-sql","output","Text-to-SQL","Text-to-SQL turns a plain-language question — \"which plans churned most last quarter?\" — into an executable SQL query against your database. An LLM is given your schema (table and column names, types, relationships) plus the question, and it generates the SELECT statement, which your app then runs and returns as a table or chart. It's the engine behind \"chat with your data\" and analytics-copilot features that let non-technical users query a warehouse without knowing SQL. For SaaS builders, it collapses the gap between a customer's question and a dashboard, and it's often cheaper to ship than building a query-builder UI. Practical note: never run generated SQL blindly — restrict the model to read-only credentials, validate against the schema, and cap rows and execution time, because a wrong join or a hallucinated column name can produce plausible-looking but wrong numbers. Feeding the model good schema descriptions and a few example queries (few-shot) sharply improves accuracy.","Text-to-SQL turns a plain-language question into an executable query against your schema — powerful, and only as safe as the guardrails you put around it.",null,[11,14,17,20,23],{"slug":12,"name":13},"chart-generation","Chart Generation",{"slug":15,"name":16},"code-generation","Code Generation",{"slug":18,"name":19},"function-calling","Function Calling (Tool Use)",{"slug":21,"name":22},"retrieval-augmented-generation","Retrieval-Augmented Generation (RAG)",{"slug":24,"name":25},"structured-output","Structured Output",[27,31,35,39,42,46,49,52,55,58,61,64],{"slug":28,"category":5,"name":29,"updated_at":30},"abstention","Abstention","2026-08-24T03:30:02+00:00",{"slug":32,"category":5,"name":33,"updated_at":34},"ai-copywriting","AI Copywriting","2026-08-24T02:46:38+00:00",{"slug":36,"category":5,"name":37,"updated_at":38},"ai-watermarking","AI Watermarking","2026-08-24T02:46:37+00:00",{"slug":40,"category":5,"name":41,"updated_at":38},"aspect-ratio-control","Aspect-Ratio Control",{"slug":43,"category":5,"name":44,"updated_at":45},"audio-generation","Audio Generation","2026-08-24T02:46:36+00:00",{"slug":47,"category":5,"name":48,"updated_at":38},"audio-super-resolution","Audio Super-Resolution",{"slug":50,"category":5,"name":51,"updated_at":45},"avatar-generation","Avatar Generation",{"slug":53,"category":5,"name":54,"updated_at":45},"background-removal","Background Removal",{"slug":56,"category":5,"name":57,"updated_at":38},"batch-image-generation","Batch Image Generation",{"slug":59,"category":5,"name":60,"updated_at":34},"brand-voice","Brand Voice",{"slug":62,"category":5,"name":63,"updated_at":34},"cfg-scale","CFG Scale (Classifier-Free Guidance)",{"slug":65,"category":5,"name":66,"updated_at":38},"character-consistency","Character Consistency"]