How to Test a GenAI Feature
Exact-match assertions fail on GenAI features because the same prompt can return different valid answers. Instead, build a versioned evaluation dataset from real and adversarial inputs, score with deterministic checks plus an LLM judge validated against human labels, check faithfulness to retrieved sources, and guard against prompt-injection. Appsierra runs these evaluations as regression checks in CI on every model change.
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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
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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.
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