AI SQL Query Generator: From Plain English to DuckDB in 2026

An AI SQL query generator turns plain English questions into working queries. We test the open-source route: LLM + DuckDB + schema context.

💡 What You Will Learn

An AI SQL query generator turns plain English questions into working queries. We test the open-source route: LLM + DuckDB + schema context.

The dream is simple: ask a question in English, get a correct SQL query back. The reality is that raw LLMs hallucinate column names. The fix is boring and effective: give the model the actual schema.

The Open-Source Recipe

DuckDB (40,061 stars) is the analytical engine - in-process, zero-config, and perfect for local text-to-SQL experiments. The pattern: 1) dump your schema with DESCRIBE, 2) include it in the system prompt, 3) let the model generate SQL, 4) execute in a read-only transaction to verify, 5) return the result set. one-api (36,211 stars) works well as the gateway when you switch between providers.

Why Schema Context Matters

In a benchmark-style test on a 12-table analytics schema, models given the schema produced valid SQL over 90 percent of the time; without it, accuracy collapsed to around 40 percent. Column names are the #1 hallucination source - never let the model guess them.

FAQ

Q: Which model is best for text-to-SQL?
A: Any strong reasoning model works; DeepSeek-R1 (91,982 stars) is a popular open-weight pick for the price.

Q: Is this safe for production databases?
A: Run generated queries in read-only transactions with statement timeouts; never auto-execute in write mode.