Evals: How Do You Know Your AI Product is Working?

Automated metrics compare model outputs against a known correct answer without human or model judgment. You define the check based on what “correct” means for the product. For a coding assistant, this might be whether the generated code runs. For a support bot, it might be whether the response includes the correct policy number.

They are fast and inexpensive, but work best when the answer is clear and verifiable. They are less useful for open-ended tasks such as summarization or creative writing. Exact word matching, for example, would treat “Your order will arrive Thursday” and “Expect delivery by Thursday” as different even though they mean the same thing.

Task-specific signals such as latency, refusal rate, and retrieval precision also belong in this category.

Benchmarks are standardized tests used to compare model versions and catch regressions across releases. HumanEval, for example, measures coding ability across model versions.

Benchmarks are less useful for domain-specific tasks that don't map to a public test.

Human evaluation is slower and more expensive, but it provides strong signal for subjective qualities such as tone, helpfulness, safety edge cases, and factual nuance.

Use inter-annotator agreement (the degree to which different reviewers score the same output similarly) to check whether your guidelines are clear. Low agreement means the labeling instructions need work before the scores are reliable.

Companies such as Scale AI and Surge AI provide trained human reviewers for this kind of evaluation.

With LLM-as-a-judge, one model grades another model's output. For example, Claude or GPT-4 might score whether a support response was helpful, accurate, and on-brand instead of requiring a human to review every response. This approach is increasingly common at companies such as Snap and Sierra.

It is faster than human labeling and cheaper at scale, but it introduces its own biases and failure modes: the judge model may be systematically lenient, or favor responses that sound confident even when they're wrong.

It should always be paired with a human-labeled calibration set to validate that the judge is actually reliable.

Before deployment, build a labeled dataset that reflects the inputs you expect in production.

• Make it representative: Cover common inputs, important user segments, and known edge cases.
• Favor quality over volume: A curated set of 500 examples with high agreement between reviewers is more useful than 50,000 noisy labels.
• Set the pass threshold in advance: Decide what result is good enough before running the eval, not after seeing the score.

Before shipping a model change, run a fixed regression suite that checks known failure modes. These checks are usually part of standard pre-release testing.

• Keep past failures in the suite: Once you find an important failure case, test it against every future version.
• Treat the suite as a floor: Passing means the model has not regressed on known issues. It does not prove the model is good overall.
• Block releases that fail: A strong aggregate score should not override a critical regression.

Once the model is live, monitor real production traffic for drift and new failure modes. Use a mix of signals:

• User behavior: Thumbs up or down, correction rate, abandonment, and overrides.
• Automated monitoring: Classifiers that scan sampled outputs for known quality or safety issues.
• Human review: Regular review of random samples and flagged interactions.

You do not need to review every output. Sample enough to spot meaningful changes before they become incidents.