AI evaluation
A different skill from checking facts before something ships — this is judging quality along real dimensions, useful any time you're comparing outputs, not just approving one.
Grading, not just fact-checking
Reviewing AI Outputs covers the workflow of catching errors before content ships — a practical, publish-focused habit. This is a broader, more general skill: judging whether an AI response is actually good, along several distinct dimensions, useful anytime — comparing two tools' answers to the same question, judging whether a piece of reasoning actually holds up, or benchmarking a new tool against one you already trust.
Five dimensions worth judging separately
Accuracy
Are the specific, checkable claims actually correct?
Coherence
Does the response hang together, or contradict itself somewhere in the middle?
Hallucination risk
Are there specifics — names, numbers, sources — that sound precise but can't actually be verified?
Logic
Does the conclusion actually follow from the reasoning shown, or just from confident phrasing?
Quality
Beyond correct — is it actually well-suited to what was asked, in tone and depth?
Judging these separately matters — a response can be accurate and still poorly reasoned, or beautifully written and quietly wrong. Collapsing all five into one gut "this seems fine" is exactly how a flawed response slips through.
A simple rubric worth using
For anything worth a real evaluation, score each of the five dimensions independently — even an informal pass/fail per dimension beats one overall impression. The value isn't the score itself, it's being forced to actually check each dimension on its own rather than letting a strong showing in one (usually fluency) paper over a weakness in another (usually accuracy).
Comparing two responses head to head
Put the same question to two tools, or ask the same tool twice with slightly different framing, and evaluate the two side by side on the same five dimensions rather than judging each in isolation. Differences that don't show up when reading one response alone often become obvious the moment there's something to compare it against directly.
Spotting hallucination risk specifically
The evaluation mistake that undoes the rest
Rewarding fluency instead of correctness — treating a well-organized, confident-sounding response as automatically a good one. Fluency is genuinely easy for these systems to produce and says almost nothing about the other four dimensions on its own. If a rubric only ever produces high scores for polished writing, it's measuring the wrong thing.
The short version
Evaluating an AI response well means resisting the pull toward one overall impression and actually checking accuracy, coherence, hallucination risk, logic, and quality as separate questions — because a response can ace one and quietly fail another, and fluent writing is the easiest of the five to fake. Use this any time you're comparing outputs or judging whether reasoning actually holds up, not just before something goes out the door. For the narrower workflow of catching errors before publishing specifically, see Reviewing AI Outputs.