A gentle introduction to LLM evaluation
New to evaluating LLM apps? Start here. What evaluation is, why 'it looks good' isn't a strategy, and the smallest useful loop you can stand up this week — no framework, no jargon.
TL;DR: LLM evaluation is measuring whether your app's outputs are actually good, instead of eyeballing a few and hoping. The smallest useful version is three things: a handful of real test cases, one or two metrics that match how your app fails, and a habit of re-scoring on every change. You can start this week, and you should — because "it looked fine when I tried it" is how quiet regressions ship.
If you're building on an LLM and haven't set up evaluation yet, this is the on-ramp. No framework, no leaderboards — just what evaluation is, why it matters, and the least you can do to start.
Why "it looks good" fails
The trap every LLM app falls into: you try a few prompts, the outputs look great, you ship. Then a prompt tweak or a model upgrade silently makes it worse, and you find out from a user. Manual spot-checking doesn't scale and doesn't repeat — you can't try the same fifty cases by hand on every change, and you can't compare two versions fairly from memory. Evaluation replaces the vibe with a number you can track.
What evaluation actually is
At its core, evaluation is a loop:
- Test cases — inputs you run through your app.
- Metrics — a way to score each output.
- Comparison — run the cases, get scores, compare to last time.
That's it. Everything fancier is a refinement of those three parts. (When you're ready to build it out properly, see how to build an LLM evaluation framework.)
Picking your first metrics
Score what matters for your app, not everything. Two kinds:
- Deterministic — a rule decides it: does the output match the expected answer, contain a required field, parse as valid JSON? Free and exact — use it wherever the correct answer is checkable. (See deterministic vs. LLM-judge metrics.)
- LLM-as-a-judge — for open-ended quality with no single right answer: is the reply helpful? Is it grounded in the source? A model scores it against a rubric. (See LLM-as-a-judge.)
Start with one or two that map to how your app actually fails. A RAG app? Score whether answers are grounded. A support bot? Score helpfulness and correctness.
Your first dataset
Don't overthink it. Ten to twenty real cases beat a giant synthetic set:
- Grab inputs from real usage if you have any.
- Add the obvious edge cases you already worry about.
- Include a few where the app should refuse or say "I don't know."
The dataset grows over time — every time you catch a new failure, it becomes a case. (No traffic yet? See synthetic data generation to seed it, and LLM evaluation for startups for the early-stage version.)
The habit that makes it work
Evaluation isn't a one-time audit — it's a habit. Re-score on every prompt change and every model change, and tie each score to the version that produced it, so you can answer "did that change help?" (See why A/B test LLM prompts.) Then, once you have users, score real production traffic too — because your test set only covers what you thought to write, and reality always adds cases. (See run LLM evals on production traces.)
The whole thing, in one table
| Part | Smallest useful version |
|---|---|
| Test cases | 10–20 real inputs |
| Metrics | 1–2 that match your failure modes |
| Cadence | Re-score every prompt/model change |
| Next step | Score production traffic once you have it |
How Currai fits
Currai is that loop without the setup: it traces your app's real runs, scores each one against deterministic checks and LLM-judge rubrics, and ties every score to the prompt and model version behind it — offline and on production traffic, with one rubric across both. You start with the small loop and grow the dataset from real failures automatically. See the LLM evaluation playbook and traces and evals in one place, or start with Currai free.
Frequently asked questions
What is LLM evaluation?
Measuring whether your LLM app's outputs are actually good — with test cases, metrics, and comparison over time — instead of manually eyeballing a few outputs. It's how you catch quality regressions before users do.
Do I need a big dataset to start?
No. Ten to twenty real test cases that match how your app is actually used beat a large synthetic set. Grow the dataset from real failures as you catch them.
What metrics should a beginner use?
One or two that map to your failure modes. Use deterministic checks (exact match, schema validity) where the answer is checkable, and an LLM-as-a-judge for open-ended quality like helpfulness or groundedness.
When should I start evaluating?
Before you ship anything you can't afford to silently break — which is almost everything. Start with a small offline loop now, and add production evaluation once you have real traffic.