I’m unhappy with manual spreadsheet reviews for LLM outputs

That’s a very common pain point. Manual spreadsheet review tends to break down when volume grows, criteria drift, or reviewers aren’t consistent.

A better path is usually to move to a lightweight evaluation system with:

1. Clear rubrics

   • Define what “good” means for each output type.
   • Use 3–5 scored dimensions max, like correctness, completeness, tone, and policy compliance.

2. Structured review format

   • Replace freeform spreadsheet notes with fixed fields:
     • pass/fail
     • severity
     • error category
     • short rationale
   • This makes analysis much easier.

3. Sample-based evaluation

   • Don’t review everything manually.
   • Use stratified sampling: high-risk cases, edge cases, and a random baseline.

4. Automated checks first

   • Use rules or scripts for obvious failures:
     • missing fields
     • format violations
     • length limits
     • banned content
   • Reserve human review for judgment calls.

5. Versioned test sets

   • Keep a stable benchmark set so you can compare model/prompt changes over time.
   • Track regressions instead of re-litigating every output.

6. Dashboarding instead of spreadsheets

   • Aggregate scores by prompt/model/version.
   • Add filters for error type, reviewer, segment, and severity.
   • This helps spot patterns quickly.

7. Inter-rater reliability

   • If multiple people review, measure agreement.
   • If reviewers disagree a lot, the rubric needs tightening.

If you want, I can help you design one of these:

• a review rubric
• a better spreadsheet schema
• a lightweight evaluation pipeline
• or a dashboard/reporting setup for LLM outputs.