How to Evaluate an LLM

You can't improve what you don't measure. Here's how to evaluate an LLM on your actual use case—building an eval set that tells you what really works.

Why evaluate on your own data

Benchmarks measure general capability, not your specific task. A benchmark leader may underperform on your use case—so evaluate on your own data to know what works. This is the discipline behind LLM evaluation explained.

Build an evaluation set

Element	What to include
Real inputs	Representative of production
Expected outputs	Or clear quality criteria
Edge cases	Tricky and adversarial

The set should reflect what the system will actually face.

Define metrics that matter

Pick metrics for your task—accuracy, relevance, safety, format adherence—not generic scores. Define what "good" means before testing, per how to measure AI success.

Combine human and automated

Use automated checks where possible (exact matches, format, safety filters) and human review for subjective quality. Both are needed for a full picture.

Use it to compare and improve

A good eval set lets you compare models and prompts, catch regressions, and improve systematically—not guess. It's how you choose a model and improve RAG accuracy.

Evaluate continuously

Evaluation isn't one-time—re-run it as you change prompts, models, or data, and pair it with production monitoring to catch drift.

Why FISTA

FISTA Solutions builds AI with rigorous evaluation—eval sets on your data that prove quality and catch regressions—through AI enablement, backed by a verified 99.9% uptime record.

Building evaluation into your AI? Talk to FISTA.