AI content quality control is the part of the workflow that most teams skip until something embarrassing goes live. At Choco Media, we run every AI-assisted piece through a structured QA checklist before it reaches a client — and the checklist exists because, at some point, we skipped it and paid the price. This post walks through the 12 checks we now run on every AI draft, why each one matters, and how to build this layer into your own process without adding two hours to every piece.
If you’re publishing AI-assisted content regularly — blog posts, emails, social copy, landing pages — this is for you. The goal isn’t to distrust AI. It’s to catch the specific, predictable failure modes that AI tools produce so that your output sounds like your brand, not like everyone else’s.
By the time you finish reading, you’ll have a working QA framework you can paste into a Notion doc and hand to anyone on your team.
Why AI content fails in predictable ways
AI writing tools are better than they were two years ago, and they’ll be better again in two years. But they fail in consistent patterns — and knowing those patterns is what makes a QA checklist useful rather than generic.
The most common failure modes we see across thousands of pieces:
- Hallucinated specificity — statistics, tool names, company details, and dates that sound authoritative but are fabricated or outdated.
- Generic openings — phrases like “In today’s rapidly changing digital landscape…” are the AI equivalent of throat clearing. Nobody reads them.
- Brand voice drift — AI defaults to a corporate, hedging tone that doesn’t match a brand’s actual voice, especially if the prompt didn’t include strong voice guidance.
- Passive constructions — AI overuses passive voice because it’s statistically common in formal writing, which is the opposite of what most marketing copy needs.
- Structural padding — sections that exist to hit word count but don’t add information. A reader notices even if they can’t name it.
The checklist below is designed around these failure modes. It’s not a stylistic preference list — it’s a catch layer for errors that are common, predictable, and easy to fix once you know what to look for.
The 12 checks we run before any AI piece leaves the agency
Check 1 — Factual claims are sourced or removed
Every statistic, percentage, named tool, case study reference, or market claim needs a source. If it can’t be verified in 60 seconds, remove it or rewrite it as a softer claim (“in our experience” or “commonly reported”). AI tools hallucinate specifics confidently, and a client who Googles a statistic and can’t find it loses trust fast.
Check 2 — The opening doesn’t start with a landscape
Read the first sentence. Does it contain any of these phrases: “in today’s,” “in the ever-evolving,” “as businesses navigate,” “in the world of,” or a variation of “things are changing”? Delete the sentence and start with the second one. The second sentence is almost always better.
Check 3 — The target keyword appears naturally in paragraph one
This is an SEO hygiene check, not a forced insertion. If the piece is about AI content quality control, the phrase should appear in the opening paragraphs because the topic naturally calls for it — not because we stuffed it in. If it reads awkwardly, the brief or draft needs a structural fix, not a workaround.
Check 4 — Brand voice match
Read three consecutive paragraphs aloud. Does it sound like the brand? For our own content, “calm, direct, first-person plural, honest” is the benchmark. For client work, we read a paragraph from their existing high-performing content first, then the AI draft, and notice the gap. Common fixes: replace hedge words (“could potentially help”), cut filler superlatives (“incredibly powerful”), and remove corporate verbs (“leverage,” “facilitate,” “utilize”).
The single biggest signal that content is AI-generated isn’t any one phrase — it’s a consistent absence of specificity. Real expertise produces details. AI produces descriptions of details.
Check 5 — Passive voice ratio
Paste the draft into Hemingway Editor or run a simple search for “is being,” “was,” “were,” “has been.” Passive constructions above roughly 10% of sentences flatten the copy. You don’t need to eliminate passive voice — you need to make it a deliberate choice, not a default.
Check 6 — Every heading is honest
Read each H2 and H3 and ask: does the section that follows actually deliver what this heading promises? AI drafts frequently generate headings that sound good but don’t match the content below them. Misaligned headings break trust with readers and confuse AI systems trying to index the page.
Check 7 — No invented internal references
AI sometimes generates phrases like “as we covered in our previous guide” or “see our full breakdown here” — pointing to content that doesn’t exist. Search the draft for “as we” and “see our” and “in our previous” and verify each reference is real. If it isn’t, remove the reference or replace it with an actual internal link.
Check 8 — Internal links are real and relevant
Every internal link in the draft should go to a page that (a) exists, (b) is relevant to the anchor text, and (c) adds value for the reader. For AI content specifically, we also check that the anchor text is descriptive — not “click here” or “learn more” — since AI sometimes defaults to generic link phrasing. This connects directly to our SEO service work, where internal linking is consistently one of the highest-leverage, lowest-effort improvements we make to a client’s site.
Check 9 — No fluency words
Run a search for the following and delete or replace every instance: “delve,” “tapestry,” “navigate” (when used abstractly), “unlock,” “unleash,” “supercharge,” “revolutionary,” “game-changer,” “synergy,” “holistic,” “leverage” (as a verb). These are the words AI reaches for because they appear frequently in formal writing. They’re also the words that signal, to any experienced reader, that the content wasn’t written by a person with a point of view.
Check 10 — The conclusion closes, not summarises
AI endings almost always summarise. “In conclusion, we’ve seen that…” is not a conclusion — it’s a loop back to the introduction. A strong closing either (a) gives the reader one thing to do next, (b) reframes the whole piece with a single sharp observation, or (c) asks a question that makes the reader think. Rewrite the conclusion if it reads as a summary.
Check 11 — Excerpt and meta description are accurate
The excerpt should be 140–155 characters, contain the target keyword, and accurately represent what the piece covers — not what AI inferred it covers. Read the piece first, then write the excerpt from scratch. Copying the AI-generated excerpt almost always produces something vague.
Check 12 — Read it at speed once more
The final check isn’t a search — it’s a read. Open the draft, read it at roughly speaking pace, and notice anywhere you slow down or re-read a sentence. Friction points are structural problems. A sentence you had to re-read is a sentence that needs rewriting. This takes three minutes on a 1,500-word piece and catches the things the previous 11 checks miss.
How we run this in practice
For short-form content (social, email snippets under 300 words), we run a compressed version: checks 1, 2, 4, 9, and 12. That’s a two-minute pass.
For long-form content — blog posts, landing pages, case studies — we run all 12. The full checklist takes 15–25 minutes for a 1,500–2,000-word piece. That time is worth it. A published error in a client deliverable costs more than the time the draft saved.
We keep the checklist in a Notion page with a checkbox template. Each piece gets its own checklist instance before it goes to review. The person who wrote the brief checks it, not the person who prompted the AI — because the brief-writer knows the intent, and proximity to the draft creates blind spots.
When to involve a second reader
For any piece that (a) includes specific claims about a client, product, or market, (b) will be bylined by someone senior, or (c) covers a sensitive topic like pricing, competitive comparison, or industry standards — we add a second-reader pass after the checklist. The checklist catches systematic errors. A second reader catches judgment errors.
Adapting the checklist for different content types
The 12 checks above were designed for long-form blog content but adapt easily:
- Email copy: Add a check for subject line accuracy (does it match the body?) and remove check 7 (invented internal references are less common in email). Check 2 becomes check 1 — email openings are even more likely to start with a generic observation.
- Social media captions: Prioritise checks 4, 9, and 12. Brand voice and fluency words matter most in short-form. Factual claims (check 1) also matter — a wrong statistic in a caption gets screenshotted.
- Landing pages: Checks 1, 3, 4, 6, 8, and 10 are the highest priority. Landing pages are persuasive documents — passive voice, bad headings, and generic conclusions all directly affect conversion rate. This connects to how we approach conversion rate optimisation: the copy layer and the CRO layer are the same problem.
- Product descriptions: Check 1 is non-negotiable. Incorrect dimensions, compatibility claims, or material specs can generate returns and support tickets. Add a spec verification step before the final read-through.
The checklist as a briefing tool, not just a review tool
One thing we’ve found over time: the best use of a QA checklist is before writing, not just after. When a writer or prompt engineer knows the checklist exists, they design the brief to avoid the failure modes from the start. You end up with drafts that pass checks 1–11 on the first pass and only need the final read-through.
This is how AI-assisted content creation compounds over time — the feedback from QA checks improves the briefs, better briefs produce better drafts, and the time cost per piece goes down while quality goes up. We typically see this happen over a 60–90 day period when a team starts using a consistent QA layer.
The checklist also makes quality legible to clients. When someone asks “how do you ensure quality on AI content,” pointing to a specific 12-step process is a better answer than “we review it carefully.”
Building the habit: why this needs to be a document, not a mental checklist
Mental checklists collapse under volume. When you’re reviewing four pieces in a morning, you’ll remember checks 1, 2, and 9 and skip the rest. The value of a written checklist isn’t that it contains secret knowledge — it’s that it’s complete.
Our recommendation: paste the 12 checks into a Notion or Google Docs template. Create a new instance for every piece. Add a checkbox to each item. Make it a delivery condition, not an optional step.
The first few times you run it, you’ll find the same errors in the same places. After a month, you’ll start writing (or prompting) in a way that avoids those errors. The checklist becomes a training tool for your own process.
A word on AI tool differences
Different models have different failure patterns. Claude tends to produce well-structured, hedged prose that needs more voice work than factual correction. GPT-4o tends to be more confident and more likely to hallucinate specific claims. Gemini varies significantly by task type. The checklist above catches failures across all of them — but if you’re using one model consistently, you’ll learn which checks matter most for that tool’s particular blind spots.
The checks don’t change. The priority order might.
What good AI content QA produces
A piece that passes all 12 checks will:
- Contain no claims you can’t defend
- Open with something worth reading
- Sound like the brand it represents
- Have internal links that work and add value
- Close with something that moves the reader forward
That’s a high bar, but it’s not a slow process once the checklist is part of the workflow. The difference between AI content that builds trust and AI content that erodes it is usually 15 minutes of structured review.
If you want to put this into practice and you’re not sure where to start, get in touch — we run this exact process for clients who are scaling AI-assisted content and want a quality layer without adding headcount.