Measure answer quality, failure cost, latency, and human review using cases drawn from actual work.
- AI evaluation
- LLM testing
- AI quality assurance
- responsible AI
Build a realistic test set
Collect representative tasks, edge cases, and examples where the correct response is to ask for clarification or abstain. Keep private or regulated information protected throughout evaluation.
AI & AutomationAI & Automation
Thoughtful decisions compound over time.
Practical product work brings technical choices back to the people and workflows they are meant to serve.
Score more than fluency
Define correctness, groundedness, completeness, and harmful-error criteria for the use case. A polished answer can still be wrong, so have knowledgeable reviewers assess consequential examples.
Set a launch threshold and monitor
Decide what quality is acceptable, what requires human approval, and how failures will be reported. Start with a limited rollout and compare outcomes to the existing workflow.
Practical application
Build an evaluation set from anonymised, representative tasks and include ambiguous requests, missing sources, and cases where abstaining is correct. Have domain reviewers score factual support and task completion, then define an explicit threshold before widening access.
