Writing With AI Without the LLM Tells — a 2026 Editing Workflow

The workflow

1. Draft cold, badly, alone

Write the whole piece with the model closed. It will be rough. That is the point: the roughness is where your voice, your specific examples and your actual opinion live. Set a timer if you need one. The draft is the part of the process that keeps you reversible.

2. Use the model to comprehend, not to compose

Paste in the source material — the paper, the changelog, the transcript — and ask questions. "What does this paper claim and what does it not claim?" "Which of these numbers is measured and which is modelled?" This is the invaluable-for-comprehension use Cantrill grants, and it produces the specific, checkable details that absence tells are made of.

3. Edit with constrained prompts

Give the model your draft and a narrow brief. The prompt matters; open-ended "improve this" produces the tells.

You are a line editor. Do not add sentences. Do not add lists.
Do not change my claims or my examples. Preserve my sentence rhythm.
Do: cut redundancy, fix grammar, flag any sentence that is vague
or unsupported with [VAGUE] or [CITE?]. Return the edited text
and a short list of what you changed.

Then a second pass, adversarial:

List every phrase in this text that a reader would recognise as
LLM-generated: filler adjectives, parallel triplets, rhetorical
questions, "not just... but also", closing summaries. Quote each
one. Do not rewrite anything.

You make the cuts. The model flags; you decide.

4. Put something at stake

Before publishing, find the sentence a reader could disagree with. If there is none, the piece is not finished. Add the number you measured, the thing you got wrong last time, the recommendation you would defend in a meeting. This is the step no model does for you and the one that removes the deepest tell.

5. Read it aloud, then run the mechanical checks

Reading aloud catches rhythm tells — the sameness of sentence length, the paragraph that should be two. Then the mechanical pass: the readability checker will show you sentence-length variance and grade level, and the word counter will tell you whether the "quick" piece became 3,000 words of filler. Neither replaces the ear; both catch what the ear tires of.

The counter-argument, taken seriously

The strongest case for letting the model write is throughput: a marketing team producing forty pieces a month cannot hand-draft all of them, and readers of a product changelog do not care about voice. That is true for text nobody reads as a person. It is false the moment the byline matters — a founder’s post, an engineer’s write-up, anything where the reader is deciding whether to trust you. Cantrill’s point is about exactly that text, and his line stands: have some confidence in your own voice, and write your own content. Where volume genuinely matters, the fix is fewer pieces with a person behind each, not more pieces with none.

Before and after: one paragraph through the workflow

Here is a paragraph of the kind that arrives when a model is asked to "write an intro about Python 3.15", followed by what survived the two prompts above and a human with a pen.

Before: "Python 3.15 isn’t just another release — it’s a game-changer for developers everywhere. 🚀 Whether you’re a seasoned professional or just starting your coding journey, this comprehensive update delivers robust improvements across the board. From lazy imports to enhanced performance, let’s dive into what makes 3.15 truly special. Ready to level up? Let’s explore!"

After: "Python 3.15 ships on 1 October. Two changes will break code that has worked for a decade: open() now defaults to UTF-8 instead of the system locale, and the garbage collector reverts to the generational design after 3.14’s incremental collector caused memory pressure in production. Everything else — lazy imports, frozendict, a faster JIT — is optional. Run your test suite under the release candidate with warnings as errors this week; on our three services that took forty minutes and found 58 call sites."

Count what changed. The emoji, the "not just", the "whether you’re", the "journey", "comprehensive", "robust", "game-changer", "dive" and "explore" are gone — that is the lexical and syntactic layer, and a filter could catch most of it. What a filter could not do is what makes the second version readable: a date, two named breaking changes with a reason, a specific instruction, and a number from actual work. The model flagged the first layer in seconds. The second layer took a person who had run the migration, which is the whole argument.

A last honesty check we apply to our own posts, this one included: could a reader tell which sentences came from the source material and which from us? If the answer is no, that is not a compliment; it means the piece has no voice to distinguish. The fix is always the same — say what you did, say what you think, and let one of those be something a reader could push back on.

Quick answers

What are the most recognisable LLM writing tells?

Emoji markers, runs of single-sentence paragraphs, "not just X but also Y", parallel triplets of abstract nouns, and a vocabulary cluster around delve, tapestry, robust and journey. The deeper tell is the absence of anything specific or contestable.

Does the cognitive-virus paper say to stop using LLMs?

No. It models adoption dynamics and identifies reversibility — retaining the ability to work unassisted — as the condition for avoiding lock-in.

What are LLMs good at in a writing workflow?

Per Cantrill: brainstorming, comprehending source material, and editing. Not authoring in your voice.

Write your next piece cold, then run the two prompts above on it and count what they flag; the number will tell you how much of your voice you had been outsourcing. If you publish on a schedule, the Content Calendar Template with AI Prompts is structured around this draft-then-edit split rather than generate-then-post, and the Reel Hooks pack applies the same at-stake rule to short-form openers. Our own take on the adjacent debate is in Is prompt engineering dead?. Every product mentioned is available at wowhow.cloud — pay once, ship forever.