Learning path · Evaluation & Quality · 74
G-Eval
Evaluation framework using LLMs with chain-of-thought rubrics to score outputs on dimensions like coherence and groundedness.
Why it matters
- Practical middle ground between human review and brittle regex.
- Widely cited for summarization and dialogue quality.
- Informs RAG faithfulness workflows.
Key ideas
- CoT scoring
- Multi-dimensional rubrics
- Form-filling prompts
G-Eval prompts the judge to reason stepwise then emit a score—often improving correlation with humans over single-shot ratings. Apply separate rubrics for faithfulness, relevance, and fluency instead of one vague "quality" number. Document temperature zero and fixed judge versions for reproducibility. Use G-Eval outputs to triage human review, not as courtroom evidence alone. Store judge prompts and reasoning traces with scores so auditors can replay why a borderline answer passed or failed. Ship only after eval gates pass on representative production failures.
Updated 2026-08-09 · Full learning path