More software products now include AI features: chat assistants, smart search, summaries, recommendations and image generation. These features do not behave like traditional code, where the same input always gives the same output. Testing them needs a different approach.
1. Define what “good” looks like
Agree with the product team what the feature should and should not do, with concrete examples of good, acceptable and unacceptable answers.
2. Build a representative test set
Collect realistic inputs from real use cases, then add edge cases: empty or very long inputs, ambiguous questions, other languages, and inputs designed to confuse the model.
3. Check accuracy and consistency
Run each input several times and with small variations in wording. Record whether answers stay accurate and consistent, and track the results across releases.
4. Test safety and fallbacks
Check that the feature refuses what it should, avoids making things up, handles errors gracefully and tells users when it is unsure.
5. Keep testing after release
Model or prompt updates can change behaviour without any code change. Re-run your AI test set on every update.
InfraWorks tests AI-powered features as part of our AI testing services. Talk to our QA team.