How to Spot AI-Generated Text and Clean It Up Before You Publish

Open almost any inbox, blog, or product page right now and there is a good chance some of the text started life as output from an AI model. That is not automatically a problem. AI tools are genuinely useful for getting a first draft down fast, working through writer's block, or summarizing a messy set of notes. The problem shows up when that draft goes out the door untouched, carrying the same handful of stylistic habits that almost every AI model seems to share. Readers are starting to notice the pattern, and once someone spots it in one place, they start seeing it everywhere else too. This guide walks through the most common tells, why they happen, and exactly how to clean each one up before you publish.

Why AI Writing Has a Recognizable Voice

Language models are trained on enormous amounts of text and then tuned to produce output that is clear, balanced, and unlikely to offend or confuse anyone. That training process tends to flatten writing toward the statistical middle. The result is prose that hedges a lot, leans on transition words like "however," "moreover," and "additionally," and likes to summarize what it just said in the next sentence. None of this is wrong, exactly. Every one of these habits shows up in good human writing too. The difference is frequency. A human writer might use a transition word once or twice in a page. An AI model can use the same handful of patterns in nearly every paragraph, and once your eye learns to catch that rhythm, it becomes hard to miss.

The good news is that almost every one of these tells is mechanical, which means it is fixable with the right tool and a few minutes of editing. The rest of this post walks through the most common ones in the order you are likely to encounter them, starting with the single most talked-about punctuation habit in AI writing.

The Em Dash Habit

The long dash that joins two related clauses into one sentence is a legitimate punctuation mark with a long history in published writing. It has also become one of the clearest fingerprints of AI-generated text, simply because of how often it shows up. Where a human writer might use it once in a long article for emphasis, AI output can lean on it two or three times per paragraph, often in places where a period, comma, colon, or a fully rewritten sentence would read more naturally.

The fix is straightforward but tedious to do by hand, especially in a long document. Read each instance in context: if the two halves of the sentence are really separate thoughts, split them into two sentences. If one half is explaining or expanding on the other, a colon often works better. If the dash is just connecting a short aside, commas usually do the job. Doing this across an entire article by hand is slow, which is exactly the kind of repetitive cleanup a dedicated tool handles in seconds.

When the Same Words Keep Reappearing

AI models tend to settle on a small set of favorite words and lean on them throughout a piece. Words like "leverage," "robust," "seamless," "ensure," and "delve" show up far more often than they would in writing where the author was reaching for varied vocabulary on purpose. The same thing happens at the sentence level. You will often see the same opening structure repeated across several paragraphs in a row, such as "It's important to note that..." followed two paragraphs later by "It's worth noting that..."

This kind of repetition is easy to miss when you are reading your own writing, because your brain fills in variety that is not actually there on the page. A second pass focused specifically on repeated words can catch things a normal read-through skips right past. Run your draft through a tool that flags words repeated close together, then go through each flagged instance and swap in a genuine synonym, or better yet, rewrite the sentence so it does not need one at all.

Checking for repetition is also useful well beyond AI cleanup. It catches the kind of accidental doubling that happens during normal editing, like leaving "the the" behind after moving a sentence around. The Duplicate Word Finder scans your text and highlights every repeated word so you can decide, word by word, whether it needs to stay or go.

Markdown Leftovers and Formatting Artifacts

When AI output gets copied straight into an email, a CMS field, a spreadsheet, or a document, the markdown formatting that the model used often comes along for the ride and does not render the way it was intended to. Double asterisks meant to produce bold text show up as literal asterisks around words. Pound signs meant to create headings appear as stray hash marks at the start of a line. Numbered lists and bullet points show up everywhere, even in places where a couple of plain sentences would read better and feel less like a slide deck.

These artifacts are a dead giveaway that text was pasted in without a cleanup pass, and they make even well-written content look unfinished. The fix depends on where the text is going. If it is headed into a plain text field, email body, or anywhere that does not render markdown, the safest move is to strip the formatting characters entirely and let the structure come through in the wording instead. If it is going into a system that does render markdown, at least make sure the syntax is consistent and that you have not ended up with a wall of single-line bullet points where a paragraph would communicate the same idea more naturally.

Find and Replace the Stock Phrases

Beyond punctuation and vocabulary, AI models reach for the same handful of stock phrases so often that they have become shorthand jokes among people who read a lot of AI output. Openers like "In today's fast-paced world," transitions like "It's important to note that," and closers like "In conclusion" or "I hope this helps" all carry almost no information on their own. They are filler that pads out a response without adding meaning, and cutting them almost always makes the writing tighter.

The challenge is that these phrases tend to repeat across a long document, sometimes with slight wording changes each time, which makes a single manual edit pass slow and error-prone. This is where batch find and replace becomes useful. Search for the exact phrase, review every place it appears in context, and either delete it outright or replace it with something specific to that paragraph. Doing this across a whole article at once, rather than phrase by phrase as you read, keeps your edits consistent and saves a significant amount of time on longer pieces.

The Find and Replace Text tool lets you search an entire document for a phrase, see every match, and swap them all out at once, which is exactly the kind of repetitive cleanup that turns a generic-sounding draft into something that reads like it was written with intent.

Editing vs. Rewriting: How to Decide

Not every piece of AI-assisted writing needs the same level of work. A short internal email that just needs to communicate a schedule change can probably get away with a light pass: fix the em dashes, cut the stock phrases, and send it. A blog post, product description, or anything with your name or your company's name on it deserves more scrutiny, because the stakes for sounding generic, or worse, factually wrong, are higher.

A useful test is to read the piece out loud. If it sounds like something you would actually say to a colleague or a customer, a light edit is probably enough. If it sounds like a press release that could have been written about any company in your industry, that is a sign the structure itself needs work, not just the word choices. In that case, keep the useful research or structure the draft gave you, but rewrite the actual sentences in your own voice. It is almost always faster to rewrite from a decent outline than to polish generic prose into something that sounds like you.

One more thing worth checking before you publish anything AI-assisted: verify any specific numbers, dates, names, or claims against a real source. Models are confident even when they are wrong, and confident, well-formatted writing is exactly the kind of thing that gets shared before anyone double-checks it.

A Quick Pre-Publish Checklist

Before you hit publish on anything that started as an AI draft, run through a short checklist. It only takes a few minutes and catches most of the issues covered above:

• Scan for long dashes and replace each one with the punctuation that fits the sentence.
• Check for words and sentence openers that repeat too often, and vary them.
• Strip any leftover markdown symbols if the text is going somewhere that will not render them.
• Search for and remove stock phrases that do not add information.
• Read the whole piece aloud once, and rewrite anything that sounds like it could describe any company.
• Double-check any specific facts, numbers, or names against a real source.

None of these steps require throwing out a useful draft. AI tools are good at getting you from a blank page to something workable fast, and that head start is worth keeping. The goal of this checklist is just to make sure the version you publish sounds like it was written by a person who cared about it, because in the end, it was.