Text-to-SQL vs Document RAG
Comparing structured querying of databases with semantic search over text.
Verdict: Use Text-to-SQL for quantitative questions over structured data (dashboards, metrics); use Document RAG for qualitative questions over unstructured text (PDFs, policies, wikis).
The Short Answer
Document RAG is designed for unstructured text. It converts a question into a semantic vector, searches a database of chunked PDFs/documents, and summarizes the results. Text-to-SQL is designed for structured data. It gives the LLM the schema of a relational database (like PostgreSQL) and asks the LLM to write a SQL query to answer the user's question with 100% mathematical precision.
Where They Differ
| Feature | Text-to-SQL | Document RAG |
|---|---|---|
| Data Type | Structured (Relational databases, CSVs) | Unstructured (PDFs, Word docs, Markdown) |
| LLM's Role | Writing executable code (SQL) | Summarizing and synthesizing text |
| Accuracy | 100% precise (if SQL is correct) | Fuzzy (relies on semantic retrieval) |
| Aggregation (Counting, Averages) | Trivial (SELECT AVG()) | Extremely difficult and error-prone |
Choose Text-to-SQL When
- The user asks quantitative questions: If a user asks "How many enterprise customers churned last month?", Document RAG will try to find a paragraph containing those words and fail. Text-to-SQL will write
SELECT COUNT(*) FROM users WHERE status='churned', returning the exact, irrefutable number. - You are building an AI data analyst: If you want users to talk to their dashboards, you must connect the LLM to your analytics warehouse via SQL.
Choose Document RAG When
- The user asks qualitative questions: If a user asks "What is our policy on remote work?", there is no SQL query that can answer that. You must retrieve the actual text of the HR handbook and have the LLM summarize it.
- Your data isn't clean: SQL requires perfectly structured, clean tabular data. If your company's knowledge lives entirely in messy Notion pages and Slack threads, you have no choice but to use Document RAG.
What People Get Wrong
People often try to force tabular, quantitative data (like a spreadsheet of sales figures) into a Vector Database to use standard RAG. This is a disaster. Vector databases cannot perform math, group by categories, or sort by dates. If your data is in rows and columns, leave it in a SQL database and use Text-to-SQL.