Understanding Natural Language to SQL

Natural Language to SQL (NL2SQL) is the process of converting human-readable questions into structured SQL queries that a database can execute. This technology bridges the gap between business users who understand their data needs and the technical SQL statements required to fulfill them.

The Multi-Agent RAG Approach

NL2SQL uses Retrieval-Augmented Generation (RAG) to produce accurate queries:

• Schema retrieval — relevant table and column definitions are fetched from a vector store
• Example matching — similar previously-validated queries are found to guide generation
• Temporal analysis — date references like "last quarter" are resolved to actual date ranges
• SQL generation — an LLM produces the query using the retrieved context
• Validation — the generated SQL is checked for correctness before execution

Why RAG Matters

Without Multi-Agent RAG, an LLM would have to guess your database schema. By grounding generation in your actual schema and curated examples, NL2SQL produces queries that are mostly accurate and safe to execute.

Trust Levels

Not all generated SQL is equal. NL2SQL assigns trust levels:

• Trusted — matched to a curated, validated query with high confidence
• Semi-trusted — moderate match, review recommended
• Untrusted — AI-generated, should be reviewed before acting on results

Upon approval by a qualified data analyst or engineer, the generated query is persisted in a vector database and leveraged in subsequent pipeline runs