What Is Text-to-SQL?
Text-to-SQL is a subfield of natural language processing that converts questions written in human language into structured SQL queries. The concept has been studied in academia for years, but recent advances in large language models have made it practical for production use.
Modern text-to-SQL systems achieve accuracy rates above 85% on complex benchmarks like Spider and BIRD, and real-world performance is even higher when the system has access to schema metadata and domain glossaries.
The Translation Pipeline
A typical text-to-SQL pipeline has four stages:
1. Schema linking maps entities in the user's question to tables and columns in the database. 2. Query sketching produces an abstract SQL template with placeholders. 3. Value filling inserts literal values, date ranges, and filter conditions. 4. Validation checks the generated query against the schema for correctness and safety.
The Role of Large Language Models
LLMs bring two critical capabilities. First, they understand context: if a user asks "Show me last quarter's numbers" after a question about revenue, the model infers that "numbers" refers to revenue figures. Second, they handle ambiguity gracefully, often asking clarifying questions or providing multiple interpretations when a query is genuinely ambiguous.
Schema-Aware Prompting
The quality of the generated SQL depends heavily on how the database schema is presented to the model. Effective strategies include providing table descriptions, column comments, sample values, and foreign key relationships. AI for Database automatically extracts this metadata and injects it into every prompt.
Handling Edge Cases
Real-world databases have quirks: nullable columns, denormalized tables, inconsistent naming conventions. A robust text-to-SQL system needs strategies for each. For instance, when a column is named is_del but means "is deleted," a glossary mapping resolves the ambiguity. When a table uses soft deletes, the system automatically adds WHERE is_deleted = false unless the user explicitly asks for deleted records.
Measuring and Improving Accuracy
We track three metrics: execution accuracy (does the query return the correct result?), exact-match accuracy (does the SQL match a gold-standard query?), and user satisfaction (did the human accept the answer?). Continuous feedback loops, where users flag incorrect results, feed back into fine-tuning and prompt engineering to steadily improve performance.