This content originally appeared on DEV Community and was authored by Alexander Zlatkov
When you ship a typical web service, you instrument it with metrics and traces. Application Performance Monitoring (APM) tells you if requests are fast, if error rates spike, and if your system is behaving as expected in production.
You also write unit tests and integration tests to catch regressions before they go live. Those two pillars, pre‑release testing and live observability, are what give you confidence in traditional software.
Now we’re implementing LLMs into everything from chatbots to workflow automation.
They’re not deterministic functions.
They don’t throw exceptions when they hallucinate.
They can produce fluent nonsense that looks plausible until you look closely.
What we need is a way to observe and test the qualitative behavior of these models and treat their outputs as first‑class citizens in our quality pipeline.
That’s where LLM evaluations (evals) come in. Evals act like unit tests and health checks for model outputs: they tell you whether the answer you got is accurate, relevant, helpful, or safe.
In the rest of this article, we’ll explore how evals work, why LLM‑as‑a‑Judge has emerged as a powerful technique for running them at scale, and how frameworks like G‑Eval implement it in practice.
What are Evals
When you ship an LLM‑powered feature, you need a way to measure how well the model and the corresponding prompts are doing on the tasks you care about.
That’s what evals are: structured tests for LLMs. Instead of checking whether a function returns the correct value, you ask questions like:
- “Did the summary capture the key facts of the article?”
- “Was the generated SQL valid and efficient?”
- “Is the chatbot’s answer helpful and non‑toxic?”
Evals can take many forms:
- Reference‑Based Metrics compare the model’s output against a ground truth. Classic examples include BLEU and ROUGE for translation and summarization.
- Unit‑Style Tests run specific prompts and assert that certain patterns or keywords appear in the response.
- Human‑in‑the‑Loop Reviews involve annotators rating outputs for quality, factuality, or safety.
- LLM‑as‑a‑Judge (discussed next) automatеs the scoring against custom evaluation criteria you define
LLM‑as‑a‑Judge: Using Models to Evaluate Models
I’ve written an in-depth article exploring this concept and how G-Eval puts it into practice — from structured chain-of-thought reasoning to probability-weighted scoring and ways to make model evaluations more consistent and interpretable.
Unfortunately, Medium’s layout (with code examples, diagrams, and academic references) doesn’t migrate cleanly here so rather than duplicate or break it, I’m linking directly to the full piece:
Medium
This content originally appeared on DEV Community and was authored by Alexander Zlatkov
Alexander Zlatkov | Sciencx (2025-11-06T16:07:49+00:00) Deep Dive into G-Eval: How LLMs Evaluate Themselves. Retrieved from https://www.scien.cx/2025/11/06/deep-dive-into-g-eval-how-llms-evaluate-themselves-2/
Please log in to upload a file.
There are no updates yet.
Click the Upload button above to add an update.