Glossary · AI & ML

RAG for Databases

RAG for databases is the use of retrieval-augmented generation to fetch the most relevant schema elements, definitions, and examples for a given question before the AI generates SQL.

A large database schema cannot be stuffed wholesale into an AI prompt, and most of it is irrelevant to any single question anyway. RAG for databases solves this with retrieval: the system indexes your tables, columns, descriptions, and past queries, then pulls only the pieces relevant to "what was our refund rate in March?"–the orders and refunds tables, not the 200 others. The model gets focused, relevant context, which directly improves SQL accuracy and keeps responses fast. This is the standard architecture for making text-to-SQL work on schemas of real-world size.

Fig — every answer ships with the tables, rows and SQL behind it.