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
Where this fits in the wider picture.
How we test AI features without shipping hallucinations
Senior people on the actual work
No layered account teams between you and the build.
Designed for the bad day
Failover, retries and load behaviour proven before launch.
Threat modelled from the start
Secure defaults, dependency hygiene, and audit trails.
Budgets, not guesses
Latency and throughput targets set and measured.
Usable by everyone
WCAG 2.2 checks built into the pipeline.
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. If the feature is an agent calling tools rather than a single prompt-response, see testing AI agents and MCP servers for the extra layers that need coverage.
Shipping an AI feature and not sure how to test it? That is a great first testing audit.
Questions about this topic.
How do you test an AI feature when output is not deterministic?
Score against criteria rather than asserting equality. Build a golden dataset of real inputs with acceptable outputs, then measure the rate of acceptable responses and track it over time.
What should an AI test suite check?
Factual grounding against provided context, output schema validity, refusal behaviour when the answer is not available, and safety under adversarial input such as prompt injection.
How often should AI features be re-tested?
On every prompt, model or retrieval change, and on a schedule regardless, because hosted models are updated by the provider without notice and quality can shift underneath you.
How ready is your product to ship and scale?
Answer 12 questions and get a maturity score across architecture, delivery, quality and security, plus the three things we would fix first. No email required to see your result.
Senior-led QA,embedded in your workflow.
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