prompt-db-audit

You validate Jinja2 templates by auditing context variables against database schemas using prefix and alias mappings, ensuring variables map to real columns and correct types to prevent runtime errors.

Orphan templates and unmapped operation types occur when code-defined operation calls lack corresponding prompt templates. Auditing flags these by verifying every set_llm_operation_type call maps to a template and checking references exist in scope.

Yes, prompt auditing supports CI checks across multi-domain prompt libraries by scanning code references and reporting errors, warnings, and recommended fixes to enforce consistency during schema migrations and onboarding.

You detect alias inconsistencies by auditing variable aliases across Python code and prompt templates, checking that schema prefixes match existing tables and verifying template references are properly integrated across domains.

The best way to prevent runtime errors is harmonizing prompt templates with schema definitions through comprehensive auditing that validates operation mappings, flags dead templates, and ensures variable types align with real database columns.

You need to audit prompt templates during schema migrations when onboarding new domain models or enforcing changes across large prompt libraries, ensuring template references remain valid and schema prefixes match updated tables.