Question 1

What makes AI product testing different from standard software testing?

Accepted Answer

Standard software testing validates deterministic behaviour – the same input produces the same output, and pass/fail criteria are clear. AI product testing evaluates non-deterministic outputs where the same input can produce different responses, and quality is often a matter of judgment rather than a binary check. It requires engineers who can assess whether an AI response is accurate, contextually appropriate, safe, and useful – not just whether an API returned a 200 OK.

Question 2

What is hallucination testing, and why does it matter?

Accepted Answer

Hallucination testing evaluates whether an AI system generates false information with apparent confidence – inventing product details, prices, policies, quotes, or facts that don't exist. For customer-facing AI, hallucinations carry direct reputational and legal risk. QA Madness engineers design specific test scenarios to probe hallucination risk across the areas most relevant to your product and use case.

Question 3

What does AI safety testing cover?

Accepted Answer

AI safety testing evaluates whether the system refuses harmful requests, avoids generating inappropriate content, protects sensitive data, and resists prompt injection attacks. The scope depends on your product's risk profile, but safety testing is recommended for any AI feature that interacts with end users.

Question 4

Can you test AI products built on third-party models like ChatGPT or Claude?

Accepted Answer

Yes. Most AI product testing engagements involve products built on top of third-party LLMs via API. The testing focuses on the integration layer – how your system prompt, retrieval logic, and product context interact with the underlying model. This is where most product-specific issues occur.

Question 5

How do you evaluate AI output quality when there's no single correct answer?

Accepted Answer

QA engineers define evaluation criteria during the assessment phase, in collaboration with your team. These criteria reflect your product's intended behaviour, brand tone, and user expectations. Where outputs require judgment, engineers assess them against these agreed standards and document their reasoning – making findings reproducible and giving your team a clear basis for prioritisation.

Question 6

How quickly can AI product testing start?

Accepted Answer

QA Madness can begin within one to three days of project kickoff. The assessment phase typically takes two to five days, depending on the complexity of the AI feature and the availability of documentation. Test execution begins as soon as scenarios are designed and the testing environment is accessible.

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