How to Evaluate LLM Features Before and After Launch

To evaluate an LLM feature, build a set of realistic test cases with clear success criteria, score outputs with a mix of automated checks, model-graded rubrics, and human review, run that suite on every change to catch regressions, and keep monitoring real usage after launch. Traditional unit tests still matter, but they cannot tell you whether a summary is faithful or a draft is helpful. Evaluation fills that gap.

Why LLM features need a different kind of testing

LLM output is non-deterministic, open-ended, and sensitive to small changes in prompts, context, and model versions. A tweak that fixes one case can quietly break five others. Without a systematic evaluation, teams end up judging quality by trying a handful of examples by hand, which feels productive but misses most failures.

Start by defining what good looks like

Before writing any tests, write the criteria. Be specific and observable. For a meeting-notes summarizer, criteria might include: covers every decision and action item, attributes actions to the right person, adds nothing that was not said, and stays under a set length. Vague goals like accurate and helpful are not testable until you break them down.

• Correctness: is the answer factually right given the source or context?
• Faithfulness: is every claim supported by the provided context, with no fabrication?
• Completeness: does it include everything required?
• Format: does it match the required structure, length, and schema?
• Safety and policy: does it avoid disallowed content, promises, or data exposure?
• Tone: does it match the voice guidelines for your product?

How to build an evaluation set

An evaluation set is a collection of inputs, with the context the system would use and the expected answer or criteria for each. Quality matters more than size. A few dozen carefully chosen cases are enough to start, and the set should grow over time.

• Use real inputs where possible, anonymized, rather than invented ones that are cleaner than reality.
• Cover the common cases in proportion, then add edge cases: ambiguous questions, missing information, long inputs, other languages, and adversarial prompts.
• Include cases where the right behavior is to refuse, ask a clarifying question, or say the information is not available.
• Tag cases by category so you can see where quality is weak, not just an overall score.
• Keep a held-out subset you do not tune against, to check you are not overfitting your prompts.

Which metrics should you use?

Deterministic checks

Use code wherever the criterion is objective: valid JSON against a schema, required fields present, length limits, banned phrases, exact-match answers for classification, and whether cited sources exist. These checks are fast, cheap, and reliable, so automate as many as you can.

Retrieval metrics

For RAG features, measure retrieval on its own. For each test question, record which sources should be found, then check whether they appear in the top results. Retrieval failures look like generation failures in the final answer, so separating them saves a lot of misdirected prompt tweaking.

Model-graded evaluation

For subjective criteria like faithfulness or helpfulness, a separate model can grade outputs against a written rubric. This scales well, but graders have biases: they can favor longer answers, prefer their own style, or be lenient. Keep rubrics narrow, ask for a judgment on one criterion at a time, require a short justification, and compare grader scores against human labels on a sample before trusting them.

Human review

Humans remain the reference point. Have domain experts review a sample regularly using the same rubric. Pairwise comparison, where reviewers choose the better of two outputs, is often more consistent than absolute scores. When several reviewers disagree often, the rubric is usually unclear and needs work.

Regression testing for prompts and models

Treat prompts, retrieval settings, and model choices as code. Version them, and run the evaluation suite on every change, ideally in continuous integration. Compare results against the current baseline by category, not only in aggregate. A model upgrade that raises the overall score can still break a critical category, such as refusals or structured output.

• Pin model versions where your provider allows it, and evaluate before adopting a new one.
• Set thresholds for critical checks that block a release if they fail.
• Run each case more than once when variability matters, and look at consistency as well as average quality.
• Track cost and latency per case alongside quality, since a better answer that is far slower may not be a better product.

Monitoring LLM quality after launch

Production traffic always contains cases your evaluation set did not anticipate. Log inputs, context, outputs, and metadata with appropriate privacy controls, and watch a set of signals:

• Explicit feedback such as ratings, and implicit feedback such as edits, retries, copy actions, and abandonment.
• Automated checks run on live outputs, such as schema validity and policy filters.
• Periodic human review of a random sample plus a sample of flagged interactions.
• Operational metrics: latency, error and timeout rates, and cost per request.

Close the loop by turning real failures into new evaluation cases. Over time, the evaluation set becomes a precise description of what your feature must handle, which makes every future change safer.

A practical starting checklist

1. Write specific success criteria for the feature.
2. Collect 30 to 100 representative cases, including edge cases and expected refusals.
3. Automate deterministic checks and, for RAG, retrieval checks.
4. Add a model-graded rubric and validate it against human labels.
5. Run the suite on every change and block releases on critical failures.
6. Monitor production, review samples, and grow the set from real failures.

In discovery, we usually build the first version of the evaluation set alongside the first prototype. It keeps conversations about quality grounded in examples, and it is the asset that lets a team improve an AI feature with confidence instead of guesswork.