Deep Dive into G-Eval: How LLMs Evaluate Themselves (medium.com)

The article explains G‑Eval, a practical framework that operationalizes “LLM evals” — structured tests and health checks for non‑deterministic model outputs — by using LLMs themselves as scalable judges. Rather than relying only on brittle reference metrics (BLEU/ROUGE) or expensive human annotation, teams can run reference‑based, unit‑style, human‑in‑the‑loop, or automated LLM‑as‑a‑judge evaluations. LLM‑as‑a‑judge comes in single‑output (score a response against criteria) and pairwise (A/B comparison) modes, and is useful for automated regression testing, model comparison, and prompt/chain optimization. Practical implementation requires choosing a judge model (often a stronger model than the candidate), defining a clear rubric, and passing prompt, outputs and references to produce metric scores; research shows modern LLM judges can align with human judgment at high rates when used carefully. G‑Eval builds on that idea by converting natural‑language evaluation instructions into an automatic chain‑of‑thought (Auto‑CoT) reasoning process so the judge generates step‑by‑step evaluation steps and then fills a scoring form. Its three components are the user prompt (task + criteria), Auto‑CoT that generates interpretable evaluation steps, and a scoring function that elicits numeric answers via a form‑filling prompt. This yields more structured, explainable judgments but has practical challenges — score clustering, inter‑run inconsistency and bias — which require careful rubric design, stronger judge models, calibration, and human spot‑checks to mitigate.