Every AI-written draft we publish at Choco Media goes through the same editing checklist before it gets a publish date. Not because we distrust the tools — we use them every day — but because editing AI content without a structured pass produces work that’s technically fine and quietly forgettable. The tells are subtle: passive voice clusters in the wrong places, sentences that claim specificity without delivering it, a tone that reads like a press release nobody asked for. This post is that checklist, made explicit. It’s for anyone who uses AI to draft content and wants to ship work that sounds like a person wrote it on purpose.
We built this over about a year of annotating our own edits — noticing which problems came back again and again regardless of the model, the prompt, or the topic. What emerged is ten checks we run in sequence. Some take thirty seconds. A couple require actual thought. Together, they take an AI draft from “publishable if you squint” to something you’d actually want your name on.
This isn’t about rewriting everything. The goal is a targeted pass, not a ground-up revision. If a draft needs that level of rescue, it usually means the brief was underspecified — which is a different problem covered in our post on AI content creation.
Check 1: Does the opening earn its keep?
AI drafts almost always open with a statement of context: “In today’s digital landscape, content marketing has become increasingly important.” This is the single most reliable signal that you’re reading an unedited AI output. It tells the reader nothing, signals nothing distinctive, and burns the only moment you have their full attention.
The fix is simple in principle and requires judgment in practice. The opening paragraph should do one of three things: pose a question the reader is actually asking, make a claim that creates useful tension, or describe a situation the reader recognises from their own work. Test it by asking: would a real person say this to open a conversation, or does it read like a product description?
- Delete any opening that leads with “In today’s [X], [Y] has become [increasingly/critically/more than ever] important.”
- Delete throat-clearing: “This article will explore…”, “In this post, we will cover…”
- Replace with a specific observation, a direct claim, or a situation the reader already knows.
- If the original opening has useful context buried in it, pull that out and use it — the observation might be fine, just not as the opening line.
Check 2: Passive voice audit
Passive voice is not inherently bad writing. It becomes a problem when AI drafts use it to avoid naming who does things — which happens constantly. “Content should be reviewed regularly.” By whom? “Decisions are made based on data.” By whom, using what data, in what process? The passive voice here isn’t stylistic; it’s evasive, and readers feel it even when they can’t name it.
Run a quick scan for “should be,” “is/are [verb]ed,” “will be,” “has been,” and “can be.” Each one is a flag to check. Some will be fine — passive voice is appropriate when the agent genuinely doesn’t matter or isn’t known. But in most marketing and operational content, there’s a “we,” a “you,” or a specific role that belongs in that sentence.
- Flag: “Content should be reviewed” → fix: “We review every draft before it goes live”
- Flag: “Results can be improved by…” → fix: “You can improve results by…”
- Flag: “This approach is recommended for…” → fix: “We recommend this for…”
What “false specificity” looks like
Related to the passive voice problem is what we call false specificity: claims that have the grammatical shape of a real data point but don’t contain one. “Brands that publish consistently see significantly higher engagement.” Significantly higher than what? Compared to whom? This pattern appears constantly in AI drafts because the model has learned that specificity signals credibility, but it produces the form without the substance.
Every time you see a comparative claim — “higher,” “faster,” “more effective,” “significantly” — ask what it’s being compared to. If there’s no answer, either cut the comparative or add a real reference.
Check 3: Tonal flatness
The most common tonal failure in AI-written content is what we call executive-blandness: everything is stated at the same level of importance, the same level of certainty, and the same level of register. There are no moments of emphasis. Nothing is surprising. Nothing is said with any particular force. The draft reads like a competent summary of what someone might say, rather than someone actually saying it.
The check here is subjective but not difficult. Read the draft aloud. Notice where you would naturally pause or stress a word. Notice where your voice goes flat because there’s nothing to stress. Those flat passages are where the edit goes.
- Add one or two sentences in each major section that carry more weight — a direct claim, a counterintuitive observation, or a concise summary that doesn’t hedge.
- Cut hedging where it’s reflexive rather than accurate: “it’s worth noting that,” “it’s important to consider,” “one thing to keep in mind is.”
- Vary sentence length deliberately. AI drafts tend to cluster at medium length. Short sentences create emphasis. Use them.
The best edit we ever made to an AI draft was deleting a paragraph and replacing it with a single sentence: “We were wrong about this for two years.” Everything that followed landed harder because of it.
Check 4: The specificity audit
Specificity is the primary difference between content that earns trust and content that fills space. AI models are good at generating the shape of specific content — lists, numbered steps, examples — but often fill those structures with generic content that could apply to any company in any industry.
The specificity audit is simple: for every example, every list item, and every claim, ask whether it could only appear in a post published by your organisation. If it could appear word-for-word in a competitor’s post, it’s not specific enough. This doesn’t mean every sentence needs a proprietary claim — it means the examples and observations should be grounded in actual experience, actual tools, actual numbers.
- Replace generic examples with specific ones: not “a content team” but “a three-person content team publishing four posts a week.”
- Add real tool names, real outputs, real failure modes from your own work.
- Where you genuinely don’t have specific data, frame it correctly: “in our experience” or “we typically see” rather than presenting it as universal fact.
Check 5: Transition quality
AI drafts use a small set of transition patterns on heavy rotation: “Furthermore,” “In addition,” “It’s also worth noting,” “Another important aspect,” “Additionally.” These transitions do the minimum work required — they signal that the next thing is also a thing — but they don’t create any sense of argument or progression. Each section feels like it was appended rather than derived.
Good transitions do more than connect. They summarise what just happened and set up what’s coming. “That covers the structure — here’s where most teams go wrong in the execution.” That’s not literature, but it’s a transition that carries the reader rather than just permitting them to continue.
- Replace “Furthermore / Additionally / Moreover” with transitions that restate the previous point briefly and pivot: “That’s the setup — the execution is where it gets complicated.”
- Check that each section follows logically from the previous one, not just thematically adjacent to it.
- If two sections can be swapped without affecting anything, one of them is probably redundant.
Check 6: The CTA problem
AI-generated calls to action are almost universally bad. “If you’re ready to take your content to the next level, contact us today.” “Start your journey towards better marketing.” These CTAs fail because they’re written for nobody in particular. They don’t name what the reader is actually trying to do, and they don’t make a credible case for why acting now makes sense.
A useful CTA is specific about what happens next, who it’s for, and what they’ll get. It doesn’t need to be long. “If you’re producing content at volume and losing editorial quality in the process, we’d be worth a conversation.” That’s a CTA that acknowledges a real situation and makes a proportionate claim.
- Delete: “take your X to the next level,” “start your journey,” “reach out to learn more.”
- Replace with: a specific situation the reader is in, a specific outcome you can offer, and a specific action that’s appropriately sized to the ask.
- Don’t put the CTA only at the end. Contextual in-body CTAs — linked naturally to the relevant service — often outperform end-of-post CTAs.
Check 7: Heading quality
Headings in AI drafts tend to be either too generic (“Benefits of X”, “How to Use Y”) or too complete — they summarise the section so thoroughly that reading the section adds nothing. Both are problems. Generic headings don’t earn a click in search or skimming. Over-complete headings undermine the payoff of reading.
The best headings do two things: they make a claim or pose a question specific enough to create mild tension, and they don’t fully resolve it. “Why your editing checklist is making your content worse” does both. “Editing checklist tips” does neither.
- Test each heading: does it make someone want to read the next paragraph, or does it function as a table-of-contents entry?
- Rewrite headings that start with “The importance of…” or “Why X matters” — these are summaries, not hooks.
- Check that H2s build a coherent argument when read in sequence. If they don’t, the structure probably needs work beyond the headings.
H3s and nested structure
AI drafts often overuse H3s as a way of filling space within a section. If an H3 contains only one paragraph, it almost certainly doesn’t need to be a heading — it should be integrated into the H2 section above it. Use H3s for genuine sub-topics that require their own navigational entry, not for any paragraph that feels like it needs a label.
Check 8: Link audit
AI drafts don’t add links. When links are added after the fact — dropped into anchor text that the model chose without link context — they tend to cluster at obvious points and miss the contextual moments where they’d actually be useful. The link audit is about matching links to reader intent, not just keyword proximity.
For a post that covers AI content workflows, linking “content brief” to your service page on AI content creation makes sense contextually. Linking it to the same page every time the phrase appears in a 2,000-word post does not. Think about where a reader is most likely to want more and least likely to feel interrupted.
- Add contextual internal links at moments of high relevance, not just high keyword density.
- External links should go to primary sources — the original study, the tool’s own documentation, the official announcement — not to other round-up posts that cite the same source.
- Check that anchor text is descriptive rather than generic: “the attribution model we use” is better than “click here” or “learn more.”
- For SEO, link to your most relevant service or pillar page at least once per post — the SEO service page for SEO posts, the paid media page for paid posts, and so on.
Check 9: Facts, figures, and named tools
AI models will confidently cite statistics that are either fabricated, misattributed, or accurate to a context that doesn’t match the claim. “Studies show that 73% of marketers…” is a pattern, not a citation. Before any statistic publishes under your name, you need to find the original source and check that it says what the draft implies it says.
The same applies to tool names, feature descriptions, and pricing. These change fast. A post that describes a tool’s feature set from training data that’s eighteen months old is going to be wrong in ways that undermine your credibility with exactly the readers who know the space well.
- Every percentage, study reference, and “research shows” claim needs a source URL before publish.
- Tool descriptions should be verified against current documentation, not assumed from what the model knows.
- If you can’t verify a statistic, cut it or reframe as “in our experience” rather than publishing an unverified claim.
- Named tool pricing should either link to a current pricing page or be removed — prices change, and you’ll be blamed for the mismatch.
Check 10: Final read for voice
The last check is the hardest to systematise and the easiest to skip when you’re in a production sprint. Read the post end-to-end, aloud if possible, asking a single question: does this sound like it was written by a person who has an opinion, or does it sound like a summary of opinions that person has read?
The difference is usually visible in three places: the opening (which we covered), the subheadings (which we covered), and the moments where the draft takes a position. AI drafts hedge positions by presenting them as “considerations” or “approaches.” Real editorial voice says: “This is what we do. Here’s why we do it that way. Here’s what we tried first that didn’t work.”
- Find two or three moments in the post where you can replace a hedge with a direct position.
- Add one observation that a reader genuinely wouldn’t find in a competing post — something from your own work, your own failure, your own discovery.
- Cut anything that exists only to fill space or hit a word count. Shorter and sharper is better than longer and padded.
Applying the checklist without slowing down
The ten checks above sound like a lot of work because they are — if you try to hold them all in your head simultaneously and apply them to a draft in one pass. In practice, we run them as two passes: a structural pass (checks 1, 7, 5, 8 — structure, headings, transitions, links) that takes about ten minutes, and a voice-and-accuracy pass (checks 2, 3, 4, 6, 9, 10 — passive voice, tone, specificity, CTA, facts, final voice read) that takes fifteen to twenty depending on draft length.
The structural pass is mostly deletion and reorganisation. The voice pass is mostly replacement and addition. Keeping them separate prevents the common failure mode of editing a sentence for tone and missing that the section it’s in doesn’t need to exist.
- Pass 1 (structural, ~10 min): opening, headings, section order, transitions, links
- Pass 2 (voice/accuracy, ~15–20 min): passive voice, false specificity, tonal flatness, facts, CTA, final read
- Total for a 2,000-word post: 25–30 minutes of focused editing
- This assumes a well-briefed draft — an underbriefed draft takes longer because you’re also filling in missing substance
If you’re producing content at scale and finding that each draft takes significantly longer than this to get to publishable quality, the problem is usually upstream — in the brief, the prompt structure, or the template. The editing layer should catch errors, not compensate for a brief that never defined what the post was trying to say. Our post on AI automation workflows covers some of the upstream process design that makes the editing layer faster.
If you’d like to see how we run this in practice — briefing, drafting, editing, and scheduling across a client’s full content calendar — we’re straightforward to talk to.