G-Eval explained: LLM-as-a-judge with chain-of-thought scoring
G-Eval turns a plain-language quality criterion into a repeatable score by having the judge reason through steps first. Here's how it works, why it beats a bare 1–5 prompt, and where it still needs calibration.
TL;DR: G-Eval is an LLM-as-a-judge method that improves reliability by making the judge generate evaluation steps from your criterion, reason through them chain-of-thought, and then score — often weighting the score by token probabilities for finer granularity. It's one of the better ways to score open-ended outputs, but it's still a judge: the criterion and the calibration decide whether the number means anything.
Scoring open-ended text — a summary, a support reply, an agent's plan — usually falls to LLM-as-a-judge. The naive version ("rate this 1–5") is noisy and arbitrary. G-Eval is a structured recipe that makes the same idea far more consistent. This post is how it works and when to reach for it.
The problem G-Eval solves
Ask a model to "rate coherence 1–5" and you get a number with no reasoning behind it, high variance, and a tendency to cluster at the top of the scale. The score isn't wrong so much as unaccountable — you can't tell why it landed there or trust it to land there again.
How G-Eval works
G-Eval adds structure in three moves:
- Generate evaluation steps — from your plain-language criterion (e.g. "coherence: how well the summary's sentences build a connected whole"), the model first produces the concrete steps a grader would follow.
- Reason, then score — the judge works through those steps chain-of-thought on the actual output before committing to a number, so the score is the conclusion of an argument rather than a reflex.
- Probability-weighted scoring — instead of taking the single emitted digit, G-Eval can weight the score by the model's token probabilities across the scale, producing a smoother, more discriminating value than a blunt integer.
The result is a score that's more stable across runs and comes with a visible rationale you can audit.
Where G-Eval fits among methods
| Method | Best for |
|---|---|
| Exact match / assertion | Closed-form answers with a golden output |
| Bare 1–5 judge prompt | Quick, low-stakes scoring; noisy |
| G-Eval | Open-ended criteria needing consistency + a rationale |
| Pairwise / arena | Ranking versions, picking a winner (details) |
Use the cheapest method that fits. If a deterministic check works, use it —
don't spend a judge on something === decides.
What G-Eval doesn't fix
G-Eval makes a judge more consistent; it doesn't make it correct. You still have to:
- Write a sharp criterion — vague in, vague out, no matter how structured the reasoning.
- Control for judge bias — verbosity, position, and self-preference bias survive chain-of-thought.
- Calibrate against humans — validate the judge's scores against human labels on a sample and refine where they diverge. (See human-in-the-loop review.)
A confident, well-reasoned score from a miscalibrated judge is still wrong — just more persuasively.
How Currai fits
Currai runs G-Eval-style, criterion-driven judges over your traces — offline test sets and live production traffic — with the reasoning kept next to the score, so every judgment is auditable rather than a bare number. You keep judge scores alongside the human labels used to calibrate them, and aggregate across prompt versions and variants to see which change actually moved quality. See the LLM evaluation playbook and run LLM evals on production traces, or start with Currai free.
Frequently asked questions
What is G-Eval?
An LLM-as-a-judge method that generates evaluation steps from your criterion, has the judge reason through them chain-of-thought, and scores the output — often weighting the score by token probabilities for finer granularity and more consistent results than a bare rating prompt.
How is G-Eval different from a normal LLM judge?
A normal judge prompt asks for a number directly. G-Eval derives explicit evaluation steps and reasons through them before scoring, which reduces variance and produces a rationale you can inspect.
Does G-Eval remove the need to validate the judge?
No. It improves consistency, not correctness. You still need a sharp criterion, controls for judge bias, and calibration against human labels on a sample.
When should I not use G-Eval?
When a deterministic check works — exact match, schema validation, a regex. Reserve judge-based scoring for genuinely open-ended criteria where there's no golden answer to diff against.