SQL Hallucination
SQL hallucination is when an AI model generates a query that looks correct but references tables or columns that do not exist, or encodes logic that does not match the question asked.
In Depth
SQL hallucination is the database-specific form of AI hallucination, and it comes in two flavors. The obvious kind fails loudly: the query references "users.signup_date" when the column is actually "created_at", and the database returns an error. The dangerous kind fails silently: the query runs, but it joined the wrong tables or filtered the wrong status values, so the number it returns is wrong while looking authoritative. The fix is grounding—giving the model the real schema as context—plus validating queries before execution and letting humans inspect the SQL. Any AI database tool that hides its generated SQL is asking you to trust hallucinations you cannot check.
How AI for Database Helps
AI for Database reduces SQL hallucination by grounding the model in your introspected schema, validating queries before they run, and showing you the SQL behind every answer.
Related Terms
Hallucination
When an AI model generates plausible-sounding but factually incorrect or fabricated information.
Grounded Answers
Grounded answers are AI responses that are backed by verifiable source data—in database tools, answers computed by running a real query against your actual database rather than generated from the model's memory.
Query Validation
Query validation is the process of checking an AI-generated SQL query for correctness and safety—verifying it references real tables and columns, parses cleanly, and stays within allowed operations—before it runs.
Schema-Aware AI
Schema-aware AI is an AI system that knows the structure of your specific database—its tables, columns, types, and relationships—and uses that knowledge to generate correct queries.
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