How do you implement LLM-powered text-to-SQL for business analytics?
Text-to-SQL enables natural language querying of databases, democratizing data access for non-technical users. Architecture: natural language input, schema understanding (provide table definitions, column descriptions, sample data), SQL generation (LLM translates question to SQL), validation (syntax check, safety validation), execution (run against read-only connection), and result presentation (format data with natural language interpretation). Schema representation: provide CREATE TABLE statements with comments, column descriptions including business meaning (not just technical names), foreign key relationships, and 3-5 sample rows per table to illustrate data content. Prompt design: include the schema, the question, and clear instructions for SQL dialect and constraints. Include few-shot examples of question-SQL pairs specific to your database for best results. Accuracy improvement: implement query decomposition (break complex questions into sub-queries), use self-correction (if SQL fails, pass error back to LLM for fixing), implement semantic layer (map business terms to table/column names via a glossary), and maintain a library of validated query patterns for common questions. Safety: use read-only database connections exclusively, implement query timeout limits (kill long-running queries after 10 seconds), restrict accessible tables/schemas, validate SQL doesn't contain mutations (INSERT, UPDATE, DELETE, DROP), and implement row limits (LIMIT 1000 on all queries). Implementation: cache generated SQL for repeated questions, implement query explanation (show the SQL and explain what it does in plain language), support follow-up questions ("now filter by last month"), and provide visualization suggestions based on result shape. Evaluation: test on a curated set of question-SQL-answer triples, measure execution accuracy (does the SQL return correct results), track query success rate in production, and analyze failure patterns for improvement.