Evaluation & Human Judgement
Checking AI output reliability
Decide whether AI-generated work is reliable enough to use in a workplace deliverable.
5 min readEvaluation
Workplace example
Before a work deliverable
Before using AI text in a report, check the key facts, remove sensitive content, show uncertainty, and make sure it fits the audience.
What this means
- •Reliable AI output has been checked against trusted sources, fits the context, and has been reviewed appropriately.
- •Reliability is not the same as fluent writing, strong formatting, or agreement with what you hoped to hear.
- •A good review checks facts, sensitivity, omissions, uncertainty, audience fit, and whether expert review is needed.
Why it matters
- •Unreviewed AI output can carry errors into reports, emails, decisions, and customer-facing work.
- •Polish can make weak reasoning harder to notice.
- •A consistent review habit protects quality and trust.
Common mistakes
- •Using the first answer because it sounds complete.
- •Checking tone but not facts.
- •Treating a confidence score as proof.
What good judgement looks like
- •Review sensitive or high-impact output first.
- •Check important facts against trusted sources.
- •Look for overclaims, omissions, and hidden assumptions.
Try this at work
- •Create a six-point review checklist for AI output.
- •Apply it to one AI-generated draft.
- •Record what changed after review.
How this helps your reassessment
- •You can identify the best signs of reliability.
- •You prioritise risk-critical checks before polish.
- •You know when output needs further review before use.