LLM evaluation guide

LLM Evaluation Guide

LLM evaluation is how you know whether a prompt, RAG system, or agent is actually improving instead of merely sounding better.

Evaluating RAG RAG Pipeline Prompt Engineering Production AI Engineering

What it is

Evaluation compares model outputs against expected behavior, source evidence, user intent, and safety requirements.

Why it matters

Without evals, every prompt change is a guess. With evals, quality can improve without breaking old cases.

How it works

Create a small set of real cases, define pass criteria, run every change against the set, inspect failures, and add production failures back into the dataset.

Common mistakes

  • Only testing happy paths.
  • Using vague scores without examples.
  • Evaluating RAG only by final answer.

Best practices

  • Evaluate retrieval and generation separately.
  • Keep a golden set of real user cases.
  • Review traces weekly.

FAQ

How many eval cases do I need first?

Start with 20-30 real cases. Add more whenever production failures appear.

Should I use an LLM judge?

Use LLM judges for scale, but keep human-reviewed examples to calibrate them.

Build It In The Roadmap

Turn this tutorial into a focused roadmap step with adjacent concepts and practice prompts.

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