RAG For Text-to-SQL Agents: How I'd Wire the Retrieval, Prompting, and Guardrails So It Does Not Corrupt Your Database
A developer on Reddit once ran a text-to-SQL agent that interpreted "show me last month's sales" as "delete all records older than 30 days and show me what remains." The query executed perfectly. No syntax errors. Clean execution. The database just lost three years of historical data because the LLM decided that "show me" and "keep only" were semantically equivalent operations. The SQL was valid. The logic was catastrophic. That story captures the core problem with text-to-SQL systems: generating syntactically correct SQL is easy in 2025. Generating SQL that matches user intent without destroying your data is where most implementations quietly fail. Every tutorial shows you how to wire up LangChain with a vector store and call it done. Nobody explains what happens when the agent hallucinates a column name, joins the wrong tables, or interprets a vague question as permission to UPDATE without a WHERE clause. Read: Let Machine Learning Turn...
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