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Remove AI Tells from Text

AI-generated text carries a small set of recognisable tells: the same dozen transition phrases, the same handful of adjective stacks, the same preference for abstract over concrete vocabulary, and the same uniform sentence rhythm across every paragraph.

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Removes the most common AI transition phrases

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Breaks uniform sentence-length patterns

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Replaces generic vocabulary with concrete alternatives

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A short field guide to the AI tells the humanizer removes

The first tell is transition vocabulary. AI output relies heavily on a small set of multi-syllable transition phrases at the start of sentences: furthermore, moreover, additionally, consequently, in conclusion, to summarise, it is important to note. These appear at three to four times the rate a careful human editor would tolerate. They serve as connective scaffolding the model uses to organise its thoughts, but they read to a human as filler. The humanizer removes most of them, replaces some with simpler connectors like but and so, and lets some paragraphs flow without explicit transitions because human writers often trust readers to follow sentence-to-sentence movement without flagging every transition explicitly.

The second tell is sentence-length uniformity. AI text tends to produce sentences in the fifteen to twenty-five word range with limited variation across a paragraph. Human writing varies more dramatically, with sentences ranging from three or four words to thirty or forty within the same passage. This rhythmic variation is part of what makes prose feel alive, and its absence is what makes AI prose feel mechanical even when no individual sentence is wrong. The humanizer breaks uniform cadence by splitting some long sentences into short ones and merging some short ones into longer compound structures, producing a paragraph with the rhythm signature of human writing rather than the uniform signature of model output.

The third tell is adjective stacking. AI output frequently stacks two or three adjectives where one specific word would do better: robust, scalable, and comprehensive solution. Comprehensive, end-to-end, and integrated platform. These stacks are technically grammatical but read as marketing-deck filler regardless of context, and they appear far more often in AI output than in human writing. The humanizer thins these stacks, often reducing three adjectives to one and choosing the most specific of the three. The result is sharper writing that signals the author was actually thinking about which word fit best, which is exactly the signal you want your prose to send.

The fourth tell is abstract-over-concrete vocabulary. AI defaults to abstract verbs and nouns when concrete ones would land harder. Leverage instead of use. Utilise instead of use. In order to instead of to. Solutions, platforms, frameworks, ecosystems where specific products, features, and services would be more grounded. The humanizer pushes these toward concrete alternatives where the meaning allows, producing prose that says what it means rather than gesturing at it. There are other smaller tells (em-dash overuse, parallel structure on every list, the specific phrase navigate the complexities), and the humanizer addresses many of them as part of the same pass. The combined effect is text that no longer trips the AI filter most readers carry without consciously noticing.

Associated Press Stylebook on plain language Hemingway Editor: short sentences and active voice OECD on artificial intelligence in education

How to use this tool

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Paste your AI text into the humanizer, pick a tone preset, and run one pass. The tool addresses the small set of phrases and patterns that signal AI authorship while preserving your facts, names, and arguments.

How It Works

Step-by-step guide to remove ai tells from text:

1
Identify the obvious tells in your draft
Before running the humanizer, take thirty seconds to scan your AI draft for the most obvious tells: count the transition phrases like furthermore and moreover, count the em-dashes, look for adjective stacks of two or three. This scan gives you a baseline for what to compare against in the output. If your draft has six furthermores and four em-dashes in three paragraphs, you will be able to see clearly whether the humanizer addressed them.
2
Open the FixTools AI Humanizer
Navigate to the AI Humanizer on FixTools in any modern browser. The free tier handles 600 characters per pass and the paid tier extends to 5,000. No installation or account is required for the free tier. The page shows an input box, a tone selector, and a Humanize button. Have your AI draft ready in another window for side-by-side reference once you have the output.
3
Paste your text and pick a tone
Paste your AI draft into the input box. Choose a tone preset that matches your destination. The neutral preset is a good default if you are unsure, as it focuses heavily on the underlying tell-removal work without applying tonal adjustments that might be wrong for your specific content. Casual and professional presets also remove tells but add tone shifts on top, which is what you want when you know the destination register.
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Run the rewrite and compare against your baseline scan
Click Humanize. When the output appears, look at it against your baseline scan from step one. Most of the transition phrases should be gone or replaced with simpler connectors. Em-dash density should be lower. Adjective stacks should be thinner. Sentence-length variation should be much more pronounced. The substance of your content should be preserved exactly. If anything has drifted in meaning, edit it directly in the output box.
5
Run a final manual scan and copy
Do one more scan of the output for any residual tells: words from your personal banned list, any remaining furthermore or moreover, any adjective stacks the humanizer left in place. Replace these by hand. The combination of humanizer pass plus manual tell scan catches close to all the obvious AI signatures. Copy the result and paste it into your destination. Add at least one concrete specific detail per section before publishing as a final layer of human signal on top of the tell removal.

Real-world examples

Common situations where this approach makes a real difference:

Editor cleaning up freelance AI-assisted submissions

An editor at a content publication accepts AI-assisted submissions from freelance writers and uses the humanizer as part of the editing workflow to remove the most obvious AI tells before publication. The publication is transparent with readers about the AI-assistance policy. The editor combines the humanizer pass with a manual scan against the publication's house style guide, which includes a banned-phrase list specific to the brand. The combined workflow produces published pieces that read as the work of careful writers, which they are, with AI as a research and drafting aid rather than as a replacement for editorial judgement.

Solopreneur producing weekly newsletter

A solopreneur drafts a weekly newsletter with AI to keep up with publishing cadence between client work, then runs each issue through the humanizer to strip the obvious AI tells. Subscribers have noted that the newsletter sounds personal and grounded, which it does because the solopreneur layers in specific weekly anecdotes and opinions on top of the humanized base. The workflow takes about an hour per week from idea to send, which sustainable for a single person running a business while publishing consistently.

PR professional drafting client statements

A PR professional uses AI to draft initial versions of client statements and press releases, then humanizes each draft to remove the templated language that would otherwise make the statements read as generic corporate boilerplate. The professional version of the humanizer preset is the default for this work, with manual editing on top to ensure the client's specific voice comes through. The combination produces statements that journalists are more likely to quote because they read as something a real spokesperson said, rather than as something a press team generated.

Job seeker writing personalised cover letters

A job seeker uses AI to draft cover letters from a base template for each role they apply to, then humanizes each letter to remove the most obvious AI patterns before sending. The output reads as a personal letter rather than as an obviously templated AI generation, which is the realistic ceiling for what AI plus humanizer can achieve in this context. The job seeker is realistic that recruiters who carefully read every letter will still see AI-assisted drafting, but the goal is letters that get past initial screens and reach a human recruiter, which the workflow achieves at scale.

When to use this guide

Use this when your AI-generated text is technically fine but you can spot obvious AI patterns (templated transitions, uniform rhythm, generic vocabulary) that you want to strip before publishing.

Pro tips

Get better results with these expert suggestions:

Build a personal banned-phrase list

Over a few weeks of working with AI drafts, build your own list of phrases and words that always signal AI to your readers regardless of the surrounding prose. Common candidates: delve, leverage, robust, comprehensive, navigate the complexities, in today's fast-paced world, unlock the power of, at the end of the day, it is worth noting that. After humanizing, scan the output for any words on your list and replace them by hand. The humanizer reduces their frequency significantly but does not always eliminate them, and a one-minute manual scan catches the rest.

Watch for ChatGPT em-dash overuse

AI models, particularly ChatGPT, use em-dashes at roughly three to four times the rate careful human writers do. The humanizer reduces this density but does not eliminate it entirely. After humanizing, scan the output for em-dashes and ask of each one whether a comma, full stop, or rewritten sentence would read more naturally. Most em-dashes can be replaced or removed, and the result almost always reads cleaner. Some writers go further and ban em-dashes entirely from their final copy as an anti-tell discipline, which is a defensible choice given how strongly em-dash density correlates with AI authorship in current model output.

Reorder sentences to break parallel structure

AI output often presents lists and parallel structures with the same syntactic pattern in each item: noun phrase comma noun phrase comma noun phrase. After humanizing, look at any lists or parallel constructions in your text and consider varying the structure between items: one verb-led, one noun-led, one starting with a prepositional phrase. This kind of structural variation reads as deliberate human editing and is one of the harder tells for a model to avoid on its own. A thirty-second edit to vary parallel structure can make a paragraph feel noticeably more hand-edited.

Trust your ear over any specific rule

No checklist of AI tells captures every signal that readers respond to. The best verification step is still reading the output aloud and trusting your ear to flag anything that sounds off. If a sentence feels like AI when you read it aloud, it probably is, even if you cannot articulate the specific tell. Rewrite those sentences in your own speaking voice. This aloud-reading habit is the catch-all that picks up the long tail of AI signals that no automated tool fully addresses, and it takes only a minute or two per page.

FAQ

Frequently asked questions

In rough order of frequency: multi-syllable transition phrases at the start of sentences (furthermore, moreover, additionally, in conclusion), uniform sentence-length cadence across paragraphs, em-dash density at three to four times the human rate, adjective stacks of two or three (robust, scalable, comprehensive), abstract verbs preferred over concrete ones (leverage, utilise), and a small set of templated phrases like navigate the complexities, in today's fast-paced world, unlock the power of, and at the end of the day. The humanizer addresses all of these directly. Less common but still recognisable tells include over-reliance on parallel structure in lists and a tendency to end paragraphs with summary sentences that add nothing.

Most but not all. The humanizer significantly reduces the frequency of the most common AI tells: it removes most templated transitions, breaks uniform cadence, thins adjective stacks, and pushes vocabulary toward concrete alternatives. A one-minute manual pass after humanization typically catches the remaining few signals that survived the rewrite, particularly words from your personal banned-phrase list. The combination of automated pass plus quick manual scan addresses close to all of the obvious tells. There is always a long tail of subtler patterns that no automated tool fully removes, and the reading-aloud habit is the best way to catch those.

Honestly, no, not as a way around an AI policy. If your school, university, or certification body restricts the use of generative AI in assessed work, running an AI draft through this humanizer does not make the submission compliant. Most institutions treat presenting AI-generated work as your own as academic misconduct regardless of whether any detector flags the output, and we cannot change that. The tool is built for contexts where AI assistance is allowed and you simply want the final text to read naturally: marketing copy, blog drafts you are editing, internal documents, personal writing. If your assignment permits AI with disclosure, disclose it. If it prohibits AI, write it yourself. We will not pretend otherwise.

No, and any tool that promises this is being dishonest with you. AI detection technology is imperfect on both sides: it produces false positives on genuinely human writing and false negatives on machine-written text, and the detectors update their models constantly. FixTools AI Humanizer is positioned as a tone and clarity editor. It varies sentence length, removes overused phrases, and tightens word choice so the writing reads more naturally. Whether any specific detector flags the output on any given day is outside our control and outside the scope of what we promise. Use the tool to improve how your draft reads to humans, not as a detector evasion product.

Sometimes yes, sometimes no, and we cannot promise either outcome. AI detection technology is imperfect and inconsistent. The same detector can flag the same text differently depending on the model version, the day, and small variations in the input. The humanizer is positioned as an editorial tool for human readers rather than as a detector evasion product. Output after a humanizer pass is meaningfully less AI-shaped than the input, but whether any specific detector flags it on any specific day is outside our control. If detector evasion is your primary goal, no honest tool can promise reliable success.

Yes, several. Conceptual tells like generic argumentation, lack of specific examples, and absence of personal opinion are not addressable by a surface-level editor because they are not surface-level patterns. They are content-level patterns that require you to actually add substance the AI did not provide. This is why the recommended workflow includes a personal-detail pass after humanization: the humanizer handles the statistical surface patterns, and you handle the content-level patterns by adding things only you could know. Both layers matter, and neither alone is sufficient for fully human-feeling writing.

Removing AI tells is a surface-level edit. It changes how the prose sounds without changing what the prose says. Full rewriting is a content-level change. It restructures arguments, adds new evidence, changes the angle of approach. The humanizer does the first kind of work, not the second. If your AI draft has weak arguments or missing evidence, the humanizer will produce a more natural-sounding version of weak arguments and missing evidence. The tool is not a substitute for thinking. It is a substitute for the surface-level editorial polish a good copy editor would apply to a draft that is already substantively right.

In most cases yes, but not always. The word furthermore is not inherently wrong, and human writers do use it occasionally. The problem is the density and predictability of its use in AI output. A single furthermore in a thousand words is fine. Three furthermores in a paragraph is an obvious tell. After humanizing, if a single furthermore survives in the output and lands naturally in its context, leaving it is fine. The judgement is about density rather than absolute prohibition, and this nuance is part of what separates competent editing from rigid checklist application.

It preserves the high-level paragraph structure of your source. Sentences may be split or merged within a paragraph, and the order of clauses within a sentence may shift, but paragraph breaks are preserved. If your source has five paragraphs, the output has five paragraphs covering the same topics in the same order. This is intentional, because paragraph structure usually reflects the deliberate organisation choice of whoever drafted the content, and rewriting at the paragraph level would step beyond editorial work into content restructuring.