AI content QA prompts for evidence and editorial review
Start with editorial strategy and expert review across your website, email, paid campaigns, community, and sales content. These AI content QA prompts help your team check evidence, brand rules, structured data, and legal flags before a named editor approves a draft.
- Six quality assurance (QA) prompts check claims, brand rules, schema, answer blocks, and legal risk.
- The extractability score is an internal editorial rubric, since Google says its AI features need no chunking.
- The final prompt turns five gate results into one ship or hold call that a human editor approves.
Use the six prompts below as a repeatable review sequence. Supply the draft, approved source text, and your brand rules; keep a named reviewer responsible for each decision.
What should AI content QA prompts check before you publish?
A pre-publish QA checklist for AI drafts covers six checks in a fixed order. You check claims, brand rules, FAQ and schema, extractability, and legal flags. The sixth check makes the ship or hold call. A draft passes when four conditions hold.
- Every claim maps to a source URL you can open and quote.
- Every sentence passes your written brand rules.
- Structured data matches the visible page text word for word.
- A named editor signs off on the final call.
| Prompt | What it catches | Output | Who signs off |
|---|---|---|---|
| 1. Claim and source checklist | Unsupported statistics, wrong dates, and broken links | Claims table with source quotes | Editor |
| 2. Brand-rule fail list | Banned words, tone drift, and wrong product names | Rule, quote, and fix table | Brand lead |
| 3. FAQ and schema readiness | Markup that differs from the visible page text | Mismatch list with fixes | SEO lead |
| 4. Answer-block extractability score | Sections whose first sentence skips the answer | Score out of 10 per section | SEO lead |
| 5. Legal and compliance flags | Invented testimonials and unproven performance claims | Flag table for legal review | Legal reviewer |
| 6. Ship or hold rubric | Open issues across the five earlier gates | One decision with open items | Content lead |
Prompts for AI content QA and fact checking you can copy
Each prompt below covers one gate and returns a list or table. Paste the draft and its sources into the tags shown. Run them in order, since later gates use earlier output.
Prompt 1. Claim and source checklist
Use this prompt to fact-check AI content claim by claim against the sources you supply.
You are a fact-checking editor. Check the draft in <draft> against the sources in <sources>.
1. List every factual claim: statistics, dates, names, product details, and quotes.
2. For each claim, give the source URL from <sources> that supports it. Write NO SOURCE when none does.
3. Quote the exact source sentence that supports each claim.
4. Flag claims that go beyond the source's sample, scope, or date as OVERSTATED.
5. List every link in the draft. Mark any URL that does not appear in <sources>.
Return a table with columns: Claim | Source URL | Source quote | Status.
Use one status per row: SUPPORTED, OVERSTATED, or NO SOURCE.
Do not add facts. Do not rewrite the draft.
<draft>[paste the draft]</draft>
<sources>[paste source text and URLs]</sources>- Run this claim verification prompt with full source text, not URLs alone.
- Open every SUPPORTED URL and confirm the quoted sentence appears on the page.
Prompt 2. Brand-rule fail list
Use this prompt as a brand voice check for AI drafts, one written rule at a time.
You are a brand editor. Check the draft in <draft> against each rule in <brand_rules>.
1. Check one rule at a time, in the order listed.
2. For each rule, quote every span that breaks it.
3. When a rule has no violations, write PASS and name what you searched for.
4. Suggest one rewritten sentence per violation.
Return a table with columns: Rule | Violation quote | Fix.
Do not rewrite text that passes.
<brand_rules>[paste your written brand rules, one per line]</brand_rules>
<draft>[paste the draft]</draft>- Paste your rules exactly as written, one rule per line.
- Ask for a quote per violation so your editor finds each span fast.
- Keep rule design out of this gate. The AirOps hub on brand governance at scale covers it.
The output reads like the table below. Each row gives your editor one quote and one fix.
| Rule | Violation quote | Fix |
|---|---|---|
| Sentence case headings | "How To Run A QA Pass" | "How to run a QA pass" |
| Second person | "Marketers need a review step." | "You need a review step." |
| Define acronyms on first use | "Check the FAQ markup." | "Check the frequently asked questions (FAQ) markup." |
| Exact product names | "Airops brand kit" | "AirOps Brand Kit" |
| Sentences of 20 words or fewer | A 31-word sentence in paragraph four | Split it into two sentences. |
Prompt 3. FAQ and schema readiness
You are a technical SEO editor. Compare the visible text in <draft> with the structured data in <schema>.
1. List every FAQ question and answer that appears on the page.
2. Confirm each question and answer in the markup matches the visible text word for word.
3. Flag any markup that describes content readers cannot see.
4. Check the title tag and meta description against the claims table in <claims>.
5. Flag FAQ answers longer than three sentences or answers that depend on earlier sections.
Return a list of mismatches, each with a fix.
Do not promise rich results or rankings.
<draft>[paste the draft]</draft>
<schema>[paste the structured data block]</schema>
<claims>[paste the Prompt 1 output]</claims>- Paste the live structured data block so the model compares real markup to real copy.
- Fix mismatches in the visible copy first, then regenerate the markup.
- Drop FAQ markup from pages where the answers no longer appear.
Answer engine optimization (AEO) covers how AI tools find and cite your pages. Structured data plays a defined role there. Our guide to schema markup for AEO explains which types fit which pages.
Prompt 4. Answer-block extractability score
Use this prompt to score answer extractability for AI search, section by section, before you publish.
You are an editor scoring answer blocks. Score each H2 and H3 section in <draft> with the rubric in <rubric>.
1. Quote the heading and the first sentence of each section.
2. Score each criterion from the rubric and give a total out of 10.
3. Flag any section whose first sentence does not answer its heading.
4. Name the lowest-scoring criterion and write one fix for it.
Return a table with columns: Section | Score | Lowest criterion | Fix.
<rubric>[paste the scoring rubric]</rubric>
<draft>[paste the draft]</draft>- Score H2 and H3 sections only, since those headings frame the answers readers scan.
- Fix the lowest criterion first, then rescore that section.
- Phrase headings as buyer questions. Our guide to question-based headings shows the pattern.
This rubric is an internal editorial heuristic. Google's AI features guide says you have no requirement to break content into tiny pieces. The score measures clarity for readers, and you can adjust the criteria.
| Criterion | 0 points | 1 point | 2 points |
|---|---|---|---|
| Heading | Vague label | Topic named | Question or clear claim |
| First sentence | Restates the heading | Partial answer | Direct, complete answer |
| Stands alone | Needs earlier sections | One outside reference | Fully self-contained |
| Structure | Dense paragraph | Short paragraphs | List or table with the key facts |
| Evidence | Unsourced claims | Source named | Source named and linked |
Prompt 5. Legal and compliance flags
Use this prompt for an AI content compliance review that routes risk to your legal reviewer.
You are a compliance reviewer. Flag risk in <draft> for a human legal reviewer. Do not give legal advice.
Quote each span that matches one of these risk types:
1. Testimonials, reviews, or customer quotes without a named, verified source.
2. Performance, earnings, or return on investment claims without evidence in <sources>.
3. Claims about what an AI tool can do or replace.
4. Superlatives such as "best" or "only," and claims about competitors.
5. Health, finance, or legal advice.
Return a table with columns: Quote | Risk type | Evidence needed | Owner.
<draft>[paste the draft]</draft>
<sources>[paste source text and URLs]</sources>- Route every flag to a named legal reviewer, since the prompt gives no legal advice.
- Cut any testimonial or customer quote you can't trace to a real person.
- Keep this gate fast and leave deep audits to counsel.
Prompt 6. Ship or hold decision rubric
Use this ship or hold decision rubric to turn five gate results into one call.
You are the final QA reviewer. Read the outputs of the five earlier prompts in <qa_results>.
1. Apply the rubric in <rubric> to each gate.
2. Return one decision: SHIP, FIX AND SHIP, or HOLD.
3. List the open items behind the decision, highest risk first.
4. Return HOLD when any legal flag or unsupported statistic stays open.
Your output is a recommendation. A named human editor makes the final call.
<qa_results>[paste the outputs of Prompts 1 through 5]</qa_results>
<rubric>[paste the ship or hold rubric]</rubric>- Hold any draft with an open legal flag or unsupported statistic, whatever the other scores say.
- Log each decision with its open items so you can spot repeat failures.
- Keep a human on the final call. Google tells site owners to manually fact-check AI content before publishing.
| Gate | Ship | Fix and ship | Hold |
|---|---|---|---|
| Claims and sources | Every claim has a quoted source | Minor wording overstates a source | Any statistic lacks a source |
| Brand rules | Zero violations | Style fixes only | Wrong product names or banned claims |
| FAQ and schema | Markup matches page text | Small wording mismatches | Markup describes content not on the page |
| Extractability | Every section scores 8 or higher | Any section scores 5, 6, or 7 | Any section scores below 5 |
| Legal and compliance | No flags | Flags cleared by legal | Any open flag |
How to run prompts for AI content QA and fact checking in order
- Attach the draft and every source document to one session.
- Run Prompt 1 and fix each claim marked NO SOURCE or OVERSTATED.
- Run Prompt 2, then edit flagged lines with these Claude humanize prompts.
- Run Prompts 3, 4, and 5 on the edited draft.
- Run Prompt 6 and send the decision to the named owner.
This pre-publish content review keeps the model on detection. Your editor owns the final gate and the disclosure call.
Key takeaways
- Order matters, because brand and schema fixes depend on verified claims.
- Quoted evidence makes every QA finding checkable by a second reviewer.
- Structured data should describe the content readers can see and meet the applicable search engine requirements.
- A written ship or hold rule turns review debates into a logged decision.
Frequently asked questions about AI content QA
Can an AI detector tell me whether a draft is safe to publish?
A publication decision needs sourced claims, brand review, and a named editor. An AI detector alone cannot establish factual accuracy or approval readiness.
Who should approve AI-assisted content before it goes live?
A named editor owns the ship or hold call. Route legal flags to counsel and schema fixes to your SEO lead.
How should a team validate FAQ schema?
Check the current search engine documentation and confirm that the marked-up questions and answers appear on the page. Valid markup does not guarantee a search display or citation.
Do blogs, landing pages, and ads need different QA prompts?
Check the current search engine documentation and confirm that each marked-up question and answer appears on the page. Valid markup does not guarantee a search display or citation.
- Blog posts lean on Prompts 1, 3, and 4.
- Landing pages lean on Prompts 2 and 5.
- Ads lean on Prompt 5 and your character limits.
AirOps for AI content QA and fact checking
AirOps is the enterprise AEO and content operations platform. Start with strategy, expert review, and the channel plan, then define the inputs, owners, and approval gates your team needs.
Use your approved Brand Kit rules and evidence library as review inputs. Keep a human editor responsible for the final release decision.