How to Score LLM Outputs with a Manual Eval Scorecard
By Rui Barreira · Last updated: 13 June 2026
A manual eval scorecard gives you a repeatable way to judge LLM outputs without sending them to another model. brevio LLM Eval Scorecard uses a weighted rubric across common quality dimensions.
Core Criteria
Start with accuracy, completeness, instruction following, safety, and clarity. These criteria apply to support answers, generated content, agents, and internal copilots.
Use Weights
Not every criterion matters equally. A legal or medical workflow should weight accuracy and safety heavily. A rewriting tool may weight clarity and instruction following more.
Score Consistently
Define what 0, 3, and 5 mean for each criterion before reviewing many outputs. Consistent definitions reduce evaluator drift and make prompt changes easier to compare.
When to Automate
Manual scorecards are best for early prompt iteration and sensitive outputs. For larger eval suites, combine human review with automated checks, golden test cases, and model-based judging where privacy permits.
Frequently Asked Questions
- Why use a manual scorecard?
- Manual scoring is transparent and works for sensitive outputs that should not be sent to a model judge.
- What criteria should I score?
- Start with accuracy, completeness, instruction following, safety, and clarity. Add domain-specific criteria when needed.
- How should weights be chosen?
- Weight the criteria that matter most for the use case. For support answers, accuracy and instruction following usually deserve higher weights.
- Does this replace automated evals?
- No. It is a lightweight rubric for reviews and prompt iteration. Production eval suites should combine tests, human review, and automated checks.