What is LLM evaluation? A guide to evals

Why evals exist

You can't improve what you don't measure. LLM evaluation (evals) is how you quantify whether a model, prompt, or agent does its job — before and after every change. Without evals you're shipping on vibes.

What evals measure

• Task accuracy — does it produce correct answers on a labeled set?
• Faithfulness / grounding — for RAG, are answers supported by the retrieved context?
• Safety — does it refuse unsafe requests and resist prompt injection?
• Regressions — did a prompt tweak quietly break something else?

How an eval works

You assemble a dataset of inputs (and ideally expected outputs), run your system over it, and score the results — with exact match, model-graded rubrics, or metric libraries. Run it in CI so every change is measured.

Free eval harnesses on ai-supply

The eval kind collects open-source harnesses for accuracy, RAG faithfulness, and red-teaming — all security-scanned. Compare results on the benchmarks page and browse the NLP and cybersecurity categories.

Treat evals as a required dependency, not an afterthought. Find a vetted harness on the marketplace.