Text-to-SQL vs RAG: can SQL replace vector retrieval for AI?

When developers discovered vector databases, many attempted to index everything in their company, including relational database dumps, CSV spreadsheets, and financial ledgers. They converted database rows into text strings like `Order ID: 1029, Customer: Acme, Amount: $450` and pushed them into vector collections.

The results in production were disastrous. When a user asks "How many orders over $400 did Acme place last November?", vector similarity search returns an arbitrary assortment of top-k chunks that happen to mention Acme and order numbers. The LLM counts the five chunks it can see in its prompt and confidently reports that Acme placed five orders, completely oblivious to the other 420 orders stored in the database.

Vector similarity search measures semantic proximity; it cannot calculate mathematical sums, group by categories, filter by timestamps, or perform relational joins. For structured business data, RAG is the wrong architecture. Text-to-SQL is the solution.

Text-to-SQL transforms natural language questions into valid, optimized SQL queries that run directly against a relational database engine like PostgreSQL or MySQL.

Instead of feeding raw data rows into the LLM context, a Text-to-SQL architecture feeds the model the schema metadata: table names, column descriptions, data types, and foreign key relationships. The LLM acts as an expert database administrator, generating a clean SQL query. The application executes this query against a secure database read replica and passes the resulting tabular summary back to the LLM to format into clear natural language.

• Schema Pruning: For databases with hundreds of tables, a retrieval step identifies only the relevant table schemas needed for the specific question.
• Query Generation & Validation: The LLM drafts the SQL query, which is validated by an AST (Abstract Syntax Tree) parser to ensure it contains only `SELECT` operations.
• Execution & Self-Correction: If the database engine returns an error (e.g. unknown column name), the agent captures the error message and rewrites the query automatically.
• Natural Language Synthesis: The resulting data rows are formatted into a concise answer with exact numbers and clear data provenance.

Allowing an artificial intelligence model to generate code that executes against your production database introduces serious security risks if not architected with defensive discipline.

Never connect an LLM to a write-enabled database connection. The database user must be granted strictly read-only permissions (`SELECT` only) on specific allowed tables and views, with explicit bans on sensitive personal identifiable information (PII) like hashed passwords or billing tokens.

Furthermore, implement strict statement timeouts (e.g. max 3 seconds) and row limits (`LIMIT 100`) to prevent the model from accidentally generating cartesian joins or unindexed table scans that could degrade database performance.

In mature enterprise architectures, you do not choose between RAG and Text-to-SQL; you build an intelligent query router.

When a user submits a prompt, an agent classifier evaluates the intent: qualitative questions about policies, documentation, or meeting transcripts are dispatched to your vector RAG or CAG pipeline. Quantitative questions about user counts, financial metrics, or inventory are dispatched to your Text-to-SQL tool.

This multi-engine design gives your AI assistant complete competency over both unstructured prose and structured operational records.

• Never vectorize structured tabular records: use Text-to-SQL or structured JSON tool calling instead
• Always execute LLM-generated SQL against read-only replicas with hard execution timeouts
• Provide column-level comments in your database schema to help the LLM understand business acronyms
• Use an intelligent router agent to dispatch user questions to RAG or SQL based on query characteristics