10 Questions Every AI Reviewer Should Ask

Human review only works if reviewers know what to look for. A reviewer who reads an AI output without a structured process will miss subtle errors and catch nothing. These ten questions form a repeatable checklist that makes review fast, consistent, and effective.

Use this list as a starting point. Customize it for your domain, but keep the core structure — each question targets a different failure mode that automated checks can't reliably detect.

1. Is This Factually Accurate?

The most fundamental question. Does every factual claim in the output match an authoritative source? Check names, dates, figures, and attributions. Models frequently hallucinate plausible-sounding facts that are simply wrong. If you can't verify a claim, flag it — don't assume it's correct because it sounds confident.

2. Is Anything Important Missing?

AI outputs tend to omit context that humans take for granted. A product description might leave out a critical limitation. A summary might skip the most important finding. Compare the output against what a domain expert would expect to see. Errors of omission are harder to catch than errors of commission, but they cause real damage.

3. Does the Tone Match the Context?

Tone mismatches erode trust quickly. A clinical tone in a customer-facing email feels cold. Excessive enthusiasm in a compliance document feels unprofessional. Read the output aloud — if it sounds wrong for the audience, it probably is. Tone is subjective, but consistency matters more than any single stylistic choice.

4. Is There Evidence of Bias?

Language models absorb biases from training data. Watch for gendered assumptions, cultural stereotypes, or framing that favors one perspective disproportionately. Bias in AI outputs isn't always obvious — it often shows up in word choice, emphasis, or what gets omitted entirely. If the output makes generalizations about groups of people, it needs scrutiny.

5. Could This Output Cause Harm?

Consider the worst-case scenario. If this output is wrong, who gets hurt? Medical advice that misses a drug interaction. Financial guidance that omits risk. Legal information that's subtly incorrect. Outputs with safety implications need a higher bar of verification than brainstorming drafts.

6. Are the Sources Real and Accurate?

AI models invent citations with alarming confidence. They'll reference studies that don't exist, attribute findings to the wrong researchers, or cite real sources but misrepresent their conclusions. Every citation in an AI output needs to be checked against the original source. This is non-negotiable for any output that will be published or shared externally.

7. Do the Numbers Add Up?

Models are unreliable with quantitative reasoning. Percentages that don't sum correctly. Growth rates that contradict the underlying data. Statistics that are misquoted or taken out of context. If the output contains any numbers, verify the math and check that the figures match the source data.

8. What Happens at the Edge Cases?

AI outputs often work well for the common case but fail on edge cases. A response that handles the typical scenario might miss exceptions, rare conditions, or unusual user needs. Think about the people who don't fit the average — will this output serve them correctly? Edge case failures are where the most frustrating user experiences come from.

9. Is This Appropriate for the Audience?

Technical jargon in a consumer-facing output. Oversimplified explanations for an expert audience. Cultural references that don't translate. The output might be accurate and complete, but still wrong for who will read it. Review means evaluating fit, not just correctness.

10. Would I Be Comfortable Putting My Name on This?

The ultimate gut check. If this output were published under your name — or your company's name — would you stand behind it? If there's any hesitation, the output isn't ready. This question catches the subtle quality issues that the other nine might miss: the awkward phrasing, the slightly misleading framing, the thing that feels off but you can't quite articulate why.

Making the Checklist Stick

A checklist only works if people actually use it. Here are three ways to make it stick:

• Embed it in your review UI — Don't make reviewers memorize the questions. Put them on screen during the review process.
• Score each question — Give reviewers a simple pass/fail for each item. This creates data you can analyze to find patterns.
• Track which questions catch the most errors — Over time, you'll learn which failure modes are most common in your system and can adjust your prompts or model accordingly.

The goal of a review checklist isn't to slow reviewers down — it's to make sure they catch what matters. A focused 5-minute review with a good checklist beats a 20-minute review where the reviewer doesn't know what to look for.