eval-harness

You can validate markdown structure by running a deterministic linting gate that checks frontmatter validity, section presence, and keyword constraints against declarative JSON rubrics before qualitative LLM review. This filters out structurally malformed content during pre-commit or CI stages.

To enforce word count limits and banned buzzwords in markdown documents, apply quality guardrails using regex and section extraction to check word and sentence count bounds alongside keyword blocklists, ensuring brand consistency and readability.

Yes, you can use a deterministic structural linter to check positioning documents. It programmatically validates required sections and frontmatter fields using JSON rubrics, operating as a pre-commit gate to filter out structurally malformed content before expensive LLM review.

You can lint client proposals during a CI/CD process by running a structural validation script against the target directory. It extracts sections and checks for required frontmatter fields and citation tags, generating a structural quality report to catch missing sections.

You should not use deterministic structural validation when evaluating the qualitative content, tone, or semantic meaning of markdown artifacts, as it only enforces structural rules, section presence, and keyword constraints rather than assessing content quality.