NL-to-SQL Complexity Calculator
Assess the complexity and risk of building a natural language to SQL system over your enterprise data. Get a recommended architecture pattern and identify key risks before you build.
Your Schema & Requirements
Schema Complexity
Expected Query Types (select all that apply)
Semantic Assets Available
Security & Compliance
Data Quality
Configure your schema and click Assess
Complexity score, risk level, and recommended architecture will appear here
NL-to-SQL Architecture Guidance
- Never let LLMs generate unrestricted SQL against PII or multi-tenant data. Always enforce RLS and mandatory filters at the query compiler layer — not in the LLM prompt.
- Business metric queries require a semantic layer. Metrics like "revenue", "churn rate", or "conversion" have organisation-specific definitions. Without a semantic layer, the LLM will hallucinate the logic every time.
- Schema annotation is the highest-ROI investment. Adding column descriptions reduces schema-linking errors by 30–60% in production benchmarks.
- Large schemas need RAG, not prompt stuffing. Above ~150 tables, full-schema prompting exceeds practical context limits. Use a schema retriever to inject only relevant tables.
- Always validate generated SQL before execution. At minimum: syntax check, table/column existence check, and a SELECT-only guard. Add EXPLAIN ANALYZE on staging before prod.