Test the output that matters
A fluent answer is not evidence of correctness. Evaluation should reflect the real task, users, data and consequence rather than relying only on a supplier benchmark or a few impressive demonstrations.
Create a test set before the trial, record the expected answer or acceptance rule, run the complete workflow and measure material failures after human review. Re-test when the model, prompt, data or process changes.
Build a representative evaluation set
Routine cases
Common examples establish whether the system is useful for normal work.
Difficult cases
Ambiguity, missing context, long inputs, unusual language and conflicting evidence expose limitations.
High-consequence cases
Examples where a wrong answer affects money, people, privacy, safety or a formal decision deserve separate measurement.
Misuse and attack cases
Test prohibited instructions, sensitive disclosure, prompt injection and unsafe downstream actions where relevant.
Measure more than one kind of quality
| Measure | Question |
|---|---|
| Task success | Does the output help the user complete the intended task? |
| Factuality | Are material claims supported by reliable sources or the supplied evidence? |
| Completeness | Does it omit information required for a safe or useful result? |
| Consistency | Does the result remain acceptable across repeated or equivalent inputs? |
| Fairness | Are relevant people or groups exposed to uneven quality or harmful treatment? |
| Review burden | How much skilled time is needed to find and correct failures? |
Use consequence-weighted reporting
A single average score can hide a rare but serious failure. Report critical failures separately and set a stop threshold for cases where the system must not be used.
Human review is a control only when it works
The reviewer must understand the task, see the relevant evidence, have time to check it and possess authority to reject the output. A rushed confirmation box is not meaningful oversight.
AI accuracy and evaluation FAQs
What is a good AI accuracy score?
There is no universal score. The acceptable level depends on the task, baseline, type of error, ability to detect mistakes and consequence. A low-consequence drafting aid and a high-consequence recommendation need different thresholds.
How many test cases do I need?
Use enough cases to represent the important patterns and failures in the workflow. Higher-consequence uses need stronger sampling, domain expertise and statistical or specialist evaluation.
Can citations prove an AI answer is accurate?
No. Citations help only when they exist, support the claim and come from suitable sources. Check the source itself rather than trusting a generated reference.
Continue your AI decision
Use the next guide that matches the question or risk you still need to resolve.