The idea, explained
Start with the decision the evaluation must support. “Is this model intelligent?” is too vague. “Can this support assistant answer billing-policy questions accurately, cite the supplied policy, and avoid exposing private data?” is testable. Translate that purpose into a short list of required behaviours and important failure modes.
Build a dataset of test cases that resembles actual use. Include ordinary cases, difficult edge cases, and adversarial cases designed to expose known risks. Keep the final test set separate from examples used to write prompts, tune settings, or train the system. A holdout set is data deliberately reserved for the final check. If developers repeatedly inspect it and optimize against it, the set can wear out: the system may improve on familiar tests without improving for real users.
Choose a grader for each requirement. Exact-match grading works when only one precise answer is acceptable. Rule-based grading can check a citation, required field, forbidden phrase, or numeric range. A rubric gives a human or model grader explicit criteria, such as factual support, completeness, and tone. Human review remains valuable when context or judgment matters. Automated graders should themselves be checked against trusted human ratings because a convenient grader can be inconsistent or reward the wrong behaviour.
Set acceptance thresholds before comparing versions. Report both an overall score and results by slice—a meaningful subgroup such as language, request type, or difficulty. An average can hide a serious failure in one slice. Run the same fixed suite whenever the model, prompt, retrieval source, or application code changes. That turns the eval into a regression test: a previously passing behaviour should not silently break.
An eval is evidence, not proof. NIST’s evaluation work emphasizes meaningful tasks, datasets, metrics, and comparable scoring, while Google’s guidance stresses representative, separate test data and warns that repeatedly used test sets lose reliability. Production monitoring and user feedback are still needed because real inputs and risks change.
Follow a worked example.
Suppose a team is improving an AI assistant that answers an employee handbook. They create 60 cases: 30 common policy questions, 15 ambiguous questions that should trigger clarification, 10 requests whose answer is absent from the handbook, and 5 attempts to obtain private employee information.
For each case they store the input, the relevant handbook passage, and the desired behaviour. A rule-based grader checks whether every factual answer contains a valid passage citation. Two reviewers use a three-point rubric—0 unsupported, 1 partly supported, 2 fully supported—to judge factual support. Privacy cases pass only if the assistant refuses to reveal private information. The team sets release gates in advance: at least 54 of 60 valid citations, an average support score of at least 1.8, all 5 privacy cases passing, and no slice falling below 80%.
Version A produces 55 valid citations, a 1.82 support score, passes all privacy cases, and scores 87% on ambiguous questions. Version B produces 58 citations and a 1.90 support score but fails one privacy case. Version B has the better average, yet it fails the release gate. The result is to keep Version A while investigating the privacy regression. After fixing it, the team reruns the unchanged suite and then confirms performance on a fresh holdout sample.
Where people get stuck
Common misconception: one benchmark score tells you which AI system is best. A benchmark measures performance only for its chosen tasks, data, graders, and conditions. A high average can conceal failures on rare but costly cases, and a model can be over-tuned to a public or repeatedly reused test set. Define the intended use first, keep some data unseen, inspect important slices, validate automated graders against human judgment, and combine offline evals with production monitoring.
Now try it yourself.
1. Check the core idea
A new prompt raises the overall score from 88% to 92% but lowers privacy-case performance from 100% to 90%. The release gate requires 100% on privacy cases. Should it ship?
Reveal the answer & reasoning
No. The improvement fails the pre-agreed privacy threshold, so the change is a regression even though the average is higher. The team should inspect the failed privacy example, revise the system, and rerun the same suite. If repeated tuning has made the test suite too familiar, it should also confirm the fix on fresh holdout cases before release.
Why this was added
The library covers individual metrics, overfitting and software tests, but not the end-to-end practice of evaluating generative AI with task-specific cases, graders, release thresholds and regression checks.