How to evaluate an AI application before launch

01Why evaluation decides the project

Traditional software either passes a test or fails it. AI systems give fluent answers that can still be wrong, so quality has to be measured on representative examples rather than inferred from a demo. Agreeing how quality will be measured is the most useful thing to settle before you hire an AI application development company.

02Build a test set from real work

A good test set looks like the work the system will actually do.

• Collect real questions or tasks, anonymized where needed, across easy, typical and difficult cases.
• Write the expected answer or acceptable outcome for each, reviewed by someone who does the work today.
• Include cases the system must refuse, escalate or handle with particular care.
• Keep a held-back set that the build team does not tune against.

03Agree thresholds before the build

Decide what score is good enough to launch, for each category of question or task, and which errors are unacceptable at any rate. A support assistant may tolerate an occasional unhelpful answer but not an invented policy; an agent that changes records may need a person to approve every action until it has proved reliable.

04Test failure modes deliberately

The OWASP Top 10 for Large Language Model Applications lists prompt injection first among the risks to test. Cover at least:

• Questions outside the system’s scope.
• Requests for data a user should not be able to see.
• Attempts to override the system’s instructions, including through text inside retrieved documents.
• Ambiguous requests where asking a clarifying question is the right answer.

05Keep people in the loop where it matters

Use review queues for consequential outputs, show sources so reviewers can check an answer quickly, and make it easy for users to flag a wrong one. Human review is a design decision with staffing implications, not a disclaimer at the bottom of the screen.

06Keep measuring after launch

Re-run the test set whenever prompts, models, content or tools change, and review a sample of live conversations on a schedule. Model providers update and retire models, so a system that passed in testing can change without any change on your side. The NIST AI Risk Management Framework is a useful, vendor-neutral reference for governing this over time.

07What to ask for in a proposal

Ask every partner to describe evaluation in writing; it is one of the clearest signals of real experience. For customer-facing assistants, see AI chatbot development.

• The evaluation method, and who builds and maintains the test set.
• Launch thresholds, and how results will be reported to you.
• Monitoring after launch, and how regressions are handled.
• How model usage, cost and response times are tracked.