Overview
Enterprise NL2SQL is not just prompt engineering. It needs semantic context, permission filtering, SQL validation, and answer review.
- Build a metric dictionary before opening natural-language access.
- Restrict query generation by tenant, role, and data domain.
- Turn query results into grounded business explanations.
Semantics
Start with metrics, not prompts
Reliable natural-language analytics starts with metric ownership, calculation logic, naming rules, and accepted business definitions.
- Document approved dimensions, filters, and join paths.
- Make ambiguous terms visible instead of hiding them in prompt examples.
Safety
Constrain generation before execution
Generated SQL should be checked against tenant boundaries, role permissions, table allowlists, cost limits, and unsupported analytical claims.
- Separate generation, validation, execution, and explanation into explicit stages.
- Reject unsupported joins and require review for high-risk domains.
Adoption
Deliver answers in business language
The final response should explain what changed, why it matters, where the evidence came from, and what the user can do next.
- Show query assumptions and data freshness where they affect decisions.
- Keep analysts in the loop for metric changes and sensitive conclusions.