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Rubric scoring is the method VectorLint uses when reviewing content on a quality spectrum: clarity, tone, technical accuracy, or completeness. The rule defines the criteria and rubric levels; the LLM rates the content against each criterion, and VectorLint calculates the final score.

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How it works

The rule defines criteria, each rated by the LLM on a 1–4 scale. VectorLint normalizes each rating to the 1–10 range, then combines them into a weighted average. The normalization maps the full 1–4 rating range onto 1–10:
The normalized range is 1–10, not 0–10: a rating of 1 (Poor) floors at 1.0, so even a fully failing criterion still contributes a small positive score. Each criterion carries a weight (default 1 when unset). A higher weight gives that criterion more influence over the final score:
Criteria are defined in the rule’s frontmatter: a human-readable name, an id, and an optional weight. The LLM rates the content against each criterion using the rubric described in the rule’s body. See Rule frontmatter.

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Key properties

A rule with two criteria:
VectorLint reports scores to one decimal place, so this document scores 8.3.

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Constraints

  • Strictness has no effect on rubric scoring. Strictness is a multiplier on error density and applies only to density scoring. See Strictness.
  • For documents above the chunking threshold, VectorLint reviews each chunk independently. Judge scores are averaged across chunks, weighted by chunk size. This is an implementation detail. No configuration is required.
  • Density scoring: the method used when reviewing for discrete issues instead of rating quality
  • How it works: the full review pipeline, including chunking