How to Test a GenAI Feature
To test a GenAI feature, you cannot rely on exact-match assertions because outputs vary. Build a representative evaluation dataset, score outputs against criteria like correctness, relevance, and safety using rubrics, automated metrics, or an LLM judge, add guardrails for hallucination and harmful content, and run these evaluations as regression checks in CI on every prompt or model change.
Why can't you test GenAI features like normal software?
Conventional tests assert a fixed expected output for a given input. Generative models are non-deterministic: the same prompt can produce different valid responses, so exact-string assertions fail constantly even when the feature works correctly. The output space is open-ended rather than a fixed set of values.
Testing therefore shifts from "is the output exactly X?" to "does the output satisfy the criteria we care about?", correctness, relevance, factual grounding, tone, format, and safety. This is evaluation rather than traditional assertion, and it needs its own methods.
How do you build an evaluation dataset?
Assemble a representative set of inputs the feature will face in production, including typical cases, edge cases, adversarial prompts, and known failure modes. Where ground truth exists (a correct answer, a required format), record it; where it does not, define the criteria a good answer must meet.
Keep the dataset versioned and growing: every production failure or user complaint becomes a new evaluation case. This dataset is the backbone of GenAI testing, just as a regression suite is for traditional software, and it must reflect real usage to be trustworthy.
How do you score GenAI outputs?
Use a layered scoring approach. Deterministic checks handle anything verifiable, valid JSON, required fields present, no banned terms, length limits, and these run cheaply and reliably. For semantic quality, use reference-based metrics where you have ground truth, and human review or an LLM judge with a clear rubric where you do not.
An LLM judge scores outputs against explicit criteria at scale, but validate the judge itself against human-labelled examples so you trust its verdicts. For retrieval-augmented features, also check faithfulness, that the answer is grounded in the retrieved sources rather than invented.
How do you guard against hallucination and unsafe output?
Add runtime guardrails, not just pre-release tests. Check responses for unsupported claims (hallucination), for sensitive or harmful content, for prompt-injection attempts, and for leaking private data. Ground answers in retrieved sources and prefer "I don't know" over a confident fabrication.
Treat these as ongoing evaluations, not a one-time pass. A prompt tweak or model upgrade can silently regress safety and accuracy, so run hallucination, safety, and quality evals as regression checks in CI on every prompt, model, or retrieval change, and gate releases on the results.
How do teams keep GenAI quality stable in production?
GenAI features drift as models update, prompts change, and usage evolves, so quality needs continuous evaluation rather than a single release test. Appsierra's managed pods build evaluation sets, guardrails, and regression evals for GenAI features as part of owning the quality outcome, directly de-risked by Appsierra's own evaluation platform.
Frequently asked questions
Why do exact-match assertions fail for GenAI?
Generative models are non-deterministic, so the same prompt yields different valid outputs. Exact-string assertions break even when the feature works. You instead score outputs against criteria like correctness, relevance, format, and safety.
What is an LLM judge and is it reliable?
An LLM judge is a model that scores outputs against an explicit rubric at scale. It is useful for semantic quality where no exact ground truth exists, but you must validate its verdicts against human-labelled examples before trusting it.
How do I test for hallucinations?
Build evaluation cases with known correct answers and check whether the model invents unsupported claims. For retrieval-augmented features, measure faithfulness, whether the answer is grounded in the retrieved sources, and add guardrails that prefer 'I don't know' over fabrication.
Should GenAI evaluations run in CI?
Yes. Run your evaluation set as regression checks on every prompt, model, or retrieval change, and gate releases on the results. A small prompt tweak can silently regress accuracy or safety, so automated evals catch drift early.
What is the difference between testing AI and testing of AI?
Here we focus on testing a GenAI feature, evaluating its outputs for quality and safety. Broader 'testing of AI' also covers model bias, drift, and robustness over time. Both rely on representative datasets and continuous evaluation rather than one-off checks.
Want this done for you?
Appsierra's managed pods pick the right tools and practices, then own the testing outcome — de-risked by our own evaluation platform. Start with a low-risk pilot.