Testing an AI product before production

Evaluating an AI product is not only about checking whether an answer looks correct. It is about understanding the situations in which a product can become inaccurate, ambiguous, or difficult to use — before those situations reach users.

My experience on an AI product taught me that useful quality work connects test observations to concrete product decisions.

Start with user journeys, not just the model

An LLM response can appear convincing in a demo yet be fragile within a real workflow. Testing therefore needs to cover the full experience:

• edge cases and ambiguous wording;
• incomplete or conflicting data;
• dependencies across the interface, APIs, and business tools;
• users’ operational expectations.

This broader view makes it easier to identify issues that are not purely technical, but can reduce trust or slow adoption.

Turn feedback into a shared language

An incident becomes actionable when it is described clearly: context, observed behaviour, expected behaviour, user impact, and priority level. This makes conversation across product, engineering, and operations much easier.

Test feedback also becomes more valuable when grouped by themes: response quality, journey consistency, integration, interface understanding, or documentation. This helps teams recognise recurring patterns instead of treating each signal in isolation.

Prioritise with business context

Not every gap has the same importance. Prioritisation should consider frequency, impact, user visibility, and risk to the business use case. A small but frequent issue can be more costly than a rare and dramatic one.

Key takeaway

AI product quality is a translation exercise: translating sometimes unpredictable behaviour into design, fix, or communication decisions. That is what moves an AI product toward an experience that is more reliable, understandable, and operational.