How to Know Your AI Feature Actually Works: Evaluating LLM Outputs

Traditional software is deterministic: given the same input, you get the same output, and a test either passes or fails. Language models break that assumption. The same prompt can produce different wording each time, and "correct" is often a matter of degree. So how do you know your AI feature works — and stays working when you change a prompt or swap a model? You evaluate it. Here's how.

Why "it looked good when I tried it" isn't enough

Manually eyeballing a few outputs is how every AI feature starts, and it's fine for the first hour. But it doesn't scale and it doesn't protect you. You'll change the prompt to fix one case and silently break three others. You'll upgrade the model and not notice quality dropped on an important category. Without systematic evaluation, every change is a gamble you can't see the odds on.

Step 1: Build an evaluation set

The foundation is a collection of real examples — inputs paired with what a good output looks like. This is the single most valuable asset in an LLM project.

Use real inputs. Pull from actual usage (or realistic drafts), not tidy hypotheticals. Real inputs are messier and reveal more.
Cover the hard cases. Include edge cases, ambiguous inputs, empty inputs, and the categories you most care about getting right.
Start small. Even 20–50 well-chosen examples are enormously useful. You can grow the set over time — especially by adding every failure you find in production.

Step 2: Decide how to score

Different tasks need different scoring methods. Pick the lightest one that captures what matters.

Exact / rule-based checks. When there's a right answer or a hard requirement, just assert it: is it valid JSON? does it contain the required field? is the classification correct? These are cheap, fast, and unambiguous — use them wherever you can.
Reference comparison. For tasks with a known good answer, compare the output to a reference (exact match for structured tasks, or similarity for text).
LLM-as-judge. For open-ended quality (helpfulness, tone, faithfulness), you can use a separate model to score outputs against a rubric. It's scalable and surprisingly effective — but calibrate it against human judgement on a sample, because judges have biases.
Human review. The gold standard for nuanced quality. Too slow for every change, but invaluable periodically and for building trust in your automated scores.

Step 3: Measure what you actually care about

Define the specific qualities that matter for your feature and score each separately. Common ones:

Correctness / faithfulness — is it accurate, and grounded in the provided context (not hallucinated)?
Format — does it match the required structure?
Relevance — does it address what was asked?
Safety — does it avoid disallowed content?

Tracking these individually tells you how a change helped or hurt, not just that the overall number moved.

Step 4: Run evals on every change

Wire your evaluation set into your workflow so it runs whenever you edit a prompt, change a model, or adjust retrieval. Compare the scores before and after. This turns risky guesswork into measurable progress: you keep changes that improve the numbers and reject the ones that don't.

Step 5: Close the loop with production

Evaluation isn't a one-time gate. In production:

Log inputs and outputs (respecting privacy) so you can see real behaviour.
Capture failures — thumbs-down, corrections, retries — and fold them back into your evaluation set. Yesterday's bug becomes today's test case.
Watch for drift. Providers update models; your quality can change without you touching anything. Periodic re-evaluation catches it.

Don't over-engineer it

You don't need a fancy platform to start. A folder of example inputs, a script that runs them through your feature, and a few automated checks will put you ahead of most teams. Add sophistication only when the simple version stops answering your questions.

Summary

Evaluating LLM outputs is what turns an AI demo into a dependable feature. Build a set of real examples, choose scoring methods that fit the task (rules where possible, LLM-as-judge or humans for nuance), measure the specific qualities you care about, and run the evals on every change. Then keep feeding production failures back in. You can't improve — or safely ship — what you don't measure.