How we test AI features without shipping hallucinations
AI features break the oldest assumption in testing: that the same input gives the same output. They do not. So testing them takes a different toolkit. Here is the one we use.
By Quality AboveAll · May 1, 2026 · 7 min read
AI features fail in new ways: wrong answers, made-up facts, prompt injection, and silent data drift. Test them with golden datasets, evaluation suites, guardrail checks against the OWASP LLM Top 10, and data-quality gates, not just pass/fail asserts.
Why AI breaks the old playbook
Traditional tests assume the same input gives the same output. AI features do not. The same prompt can return different text, and "correct" is often a judgment, not an exact match. So the tests have to change.
How we test AI features
- Golden datasets. A curated set of inputs and acceptable outputs, so you can measure quality as the model or prompt changes.
- Evaluation suites. Score responses on accuracy, relevance, and tone, run on every change like a regression pack.
- Guardrail tests. Probe for prompt injection, data leakage, and unsafe output, guided by the OWASP LLM Top 10.
- Hallucination checks. Verify the model is not inventing facts, especially for anything user-facing.
- Data-quality gates. The model is only as good as its data. We assert freshness, schema, and volume with data pipeline testing.
Wrap it in the basics
AI features still sit inside normal software. The contracts, APIs, and pipelines around them need the same rigor as everything else. See API and contract testing and our automation framework.
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