Definition & overview
AI evaluation is the systematic testing of model or agent behaviour against defined requirements. It can examine factual accuracy, retrieval quality, tool use, safety, cost and task completion using representative cases.
Analogy & mental model
It is a driving test with realistic routes, not one successful trip around the car park.
Keep a repeatable test set covering normal cases, missing evidence and unsafe requests.
Why AI evaluation matters
One successful demonstration does not show that a system is dependable. Evaluation exposes recurring errors, missing evidence and unsafe actions before wider deployment. The cases should reflect real user tasks and commercial consequences rather than only easy examples.
Practical business example
An enquiry assistant is tested on routine requests, spam, missing contact details and demands for unsupported pricing before it is allowed to assist staff.
Illustrative scenario, not a client case study or promised result.
How AI evaluation works
- Define measurable requirements and a representative set of expected outcomes.
- Run the workflow against normal, ambiguous, failure and adversarial cases.
- Inspect errors, revise the system and repeat the same tests after changes.
Core components & mechanisms
- Representative cases
- Explicit success criteria
- Repeatable comparison
Automated judges can be useful but should not replace human review of consequential or disputed cases.
AI evaluation vs Monitoring
Evaluation tests defined cases before or between releases. Monitoring observes behaviour in use; both are needed because production inputs may differ from the test set.
Frequently asked questions
What does AI evaluation mean?
AI evaluation is the systematic testing of model or agent behaviour against defined requirements. It can examine factual accuracy, retrieval quality, tool use, safety, cost and task completion using representative cases.
How is it different from Monitoring?
Evaluation tests defined cases before or between releases. Monitoring observes behaviour in use; both are needed because production inputs may differ from the test set.
What is a sensible first step for a small business?
Keep a repeatable test set covering normal cases, missing evidence and unsafe requests. Define measurable requirements and a representative set of expected outcomes.
What limitations should I keep in mind?
Automated judges can be useful but should not replace human review of consequential or disputed cases.
Sources & further reading
OpenAI: Evaluation best practicesOriginal Mushbloom explanation and illustrative examples. References support the underlying topic, not every business recommendation. Check current documentation before implementation.
Turn the concept into a practical decision
Discuss your website or business workflow before choosing tools or implementation. Start with the problem, approved information and appropriate human oversight.
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