LLM Checker: How to Verify If Text Was Written by AI

What Is an LLM Checker?

An LLM checker is a tool that reads a block of text and outputs a probability score indicating how likely it is that the text was generated by a large language model rather than typed by a person. Most tools work the same way from the outside: paste in text, upload a file, or in some cases connect a document, and the checker returns an overall percentage along with sentence- or paragraph-level highlighting showing which sections drove the score. You'll also see "AI content detector," "ChatGPT detector," and "LLM text detector" used more or less interchangeably — they all describe the same category of tool, built to answer the same question: did a person write this, or did a model? The category has grown alongside the models it's built to catch. Early checkers were mostly aimed at spotting GPT-3-era output in student essays; today's tools are trained against a much wider range of writing from ChatGPT, Claude, Gemini, Llama-based assistants, and dozens of smaller fine-tuned models, plus the paraphrased and "humanized" versions of that output. That range matters because a checker built narrowly around one model's writing habits tends to miss text from a different model entirely, which is part of why cross-checking a result with a second, independently trained tool matters more than trusting any single score.

How Does an LLM Text Detector Actually Work?

Most LLM text detector engines rely on two related signals: perplexity and burstiness. Perplexity measures how predictable each next word is given the words before it — language models tend to generate text that scores low on perplexity because they're statistically choosing likely next tokens, while human writing tends to include more unexpected word choices and phrasing. Burstiness looks at variation across a document: human writers naturally mix short punchy sentences with long, winding ones, and shift tone and rhythm from paragraph to paragraph, while model output tends to stay more uniform in sentence length and structure. A checker's classifier — usually a machine learning model trained on labeled examples of human and AI text — combines these signals with other stylistic features to produce a probability score, then highlights the specific spans that pushed the score higher. Some detectors add a second layer on top of perplexity and burstiness: watermark detection, which looks for a statistical pattern some model providers quietly embed in generated tokens, and stylometric fingerprinting, which compares a document's vocabulary and sentence patterns against a known sample of the same author's earlier writing. Neither is universal — watermarking only works if the originating model applied one and the text wasn't heavily edited afterward, and fingerprinting only works if a genuine writing sample exists to compare against — which is why perplexity and burstiness scoring remains the backbone most LLM checkers fall back on.

1. Text is broken into sentences or sliding windows for scoring
2. The classifier measures perplexity — how predictable each word choice is in context
3. It measures burstiness — how much sentence length and rhythm vary across the document
4. Additional stylistic features (vocabulary diversity, repetition patterns, structure) feed into the same model
5. Some tools cross-check for watermark patterns or compare style against a known writing sample
6. The combined signals produce an overall probability score plus highlighted passages

Why Isn't an LLM Checker 100% Accurate?

No LLM checker, including NotGPT's, can guarantee a perfect result, and understanding why helps you read a score correctly instead of treating it as a fact. Detectors are trained on how current models tend to write, so accuracy drifts as newer LLMs produce more varied, less formulaic output — a detector tuned on last year's ChatGPT output won't necessarily catch this year's, and providers update their models faster than most detectors can retrain against them. Paraphrasing tools and "humanizer" services are built specifically to break the perplexity and burstiness patterns detectors look for, which can pull a genuinely AI-written passage below the flagged threshold without changing the underlying source. The reverse problem is just as real: formulaic but entirely human writing — a five-paragraph essay structure, a templated cover letter, a non-native speaker writing in a simplified, consistent register — can score as predictable and get flagged even though no AI was involved. Short text adds another limit that's easy to overlook: a two-sentence caption or a single paragraph often doesn't give a classifier enough signal to score reliably, so results on very short samples should be trusted less than results on a full page or document. Translated text carries a similar problem, since translation tends to flatten some of the stylistic variation a detector relies on regardless of who wrote the original. None of this means the tool is useless; it means the number is a probability estimate that needs context, not an automatic conclusion.

A probability score from any LLM checker is evidence to investigate, not proof to act on.

How Do Teachers Use an LLM Checker in the Classroom?

Teachers typically run an LLM checker as one input into a broader academic integrity conversation, not as the sole basis for a grade decision. A practical classroom workflow starts with checking flagged submissions rather than every submission, since scanning an entire class routinely amplifies false-positive risk for students who simply write in a formal or formulaic style. When a paper does score high, the next step is reading the highlighted sections directly instead of stopping at the headline percentage — some detectors flag boilerplate structure or a strong thesis statement as "predictable" even in an otherwise clearly human essay. Comparing the flagged draft against a student's earlier, verified work — in-class writing samples, prior submissions, or version history in Google Docs — gives a stronger signal than any single score, and framing a flagged result as a conversation starter rather than an accusation keeps the process fair when the detector is wrong. It also helps to set expectations before an assignment is due rather than after: telling students up front that submissions may be checked, and what the school's policy is if a paper is flagged, tends to produce fewer disputes than introducing a checker only after a suspicious grade decision has already been made. Departments that rely on a single detector for every course also tend to run into the same blind spot across the board — pairing that primary tool with an occasional second opinion from a different detector catches cases the first one's training data happens to miss.

How Do Editors Use an LLM Checker Before Publishing?

For editors screening freelance submissions or user-generated content, an LLM checker mainly functions as a quality and disclosure filter rather than a plagiarism-style gate. Content that comes back with a high AI-likelihood score isn't automatically rejected — many publications now accept AI-assisted drafts as long as the writer discloses it and the piece is factually accurate and edited for voice — but an undisclosed high score is a reasonable trigger to ask the contributor directly. Running a spot-check on a sample of submissions rather than every single one keeps the workflow sustainable at scale, and cross-checking a flagged piece against the same writer's previous, known-human submissions helps separate a genuine AI draft from a writer whose natural style happens to read as consistent and low-perplexity. For editorial teams handling a high volume of contributor content — guest posts, marketplace listings, reader-submitted reviews — a checker also doubles as an early warning for factual risk, since AI-drafted text that hasn't been fact-checked by a human tends to correlate with unverified claims, invented sources, or generic phrasing that reads as if it could apply to any product or topic. Building a documented threshold into the editorial workflow, rather than deciding case by case, keeps the process consistent across editors and gives contributors a clear standard to write against.

Should Students Check Their Own Writing Before Submitting?

Running your own draft through a checker before submission is a reasonable way to catch an unintentional false positive before an instructor does, especially if you know your writing style leans formal, templated, or was drafted with heavy use of grammar and structure tools. If a checker flags a paragraph you wrote entirely yourself, revising it to vary sentence length and phrasing — the same qualities that separate human burstiness from model uniformity — can lower the score without changing the substance of your argument. This matters more for some students than others: writers working in a second language, students using assistive writing technology, and anyone who leans heavily on grammar-checking software tend to produce text that scores more uniformly and gets flagged more often, so a pre-submission check can be a genuine safeguard rather than an unnecessary extra step for that group. What a pre-submission check shouldn't become is a tool for iteratively rewriting AI-generated text until it slips under a detection threshold; that's a different use case than verifying your own original work, and most academic integrity policies treat it as the same violation as submitting the AI draft directly. Keeping your own drafting history — outline notes, earlier revisions, a version history in your writing app — is a more durable defense than chasing a lower detector score, since it gives you evidence of your process if a flagged score is ever questioned.

How Can You Verify an LLM Checker Result Before Acting on It?

Because no single LLM checker is definitive, the most reliable way to act on a flagged score is to treat it as a first read rather than a final one. Run the same text through a second, independently built detector — text that multiple tools flag in the same passages is a meaningfully stronger signal than one score from one tool. NotGPT's AI text detector gives a free, no-signup way to run that second check: paste the text in, and it returns an AI-likeness probability with sentence-level highlighting so you can see exactly which passages drove the score, rather than a single unexplained number. Read the highlighted spans against the full context of the document, check whether the flagged style matches how the writer normally writes, and hold off on any grading, editorial, or hiring decision until you've done at least one cross-check.

1. Run the text through a second detector, such as NotGPT's free AI text checker, before treating one score as final
2. Read the specific highlighted passages, not just the overall percentage
3. Check whether the flagged style is consistent with the writer's known past work
4. Treat agreement across multiple tools as a stronger signal than any single score
5. Recheck periodically — both LLMs and the detectors built to catch them keep changing

What Should You Look For When Choosing an LLM Checker?

With dozens of tools on the market, a few practical criteria matter more than a marketed accuracy percentage on its own. Sentence-level highlighting beats a single overall score every time, since it lets you evaluate the reasoning behind a result instead of taking a number on faith. A no-signup, free tier is worth prioritizing for anyone who only needs an occasional check — students, individual writers, and small editorial teams rarely need a page-credit subscription built for institutional volume. It's also worth checking whether a tool discloses anything about its training approach or update cadence, since a detector that hasn't been updated recently is more likely to miss newer models. Finally, consider what else the tool does in the same workflow: a checker bundled with related features, like an AI image detector for visual content or a humanize tool for revising flagged text, saves switching between separate apps when a single piece of content needs more than one kind of review.

1. Prioritize sentence-level highlighting over a single unexplained score
2. Check for a free, no-signup tier if you only need occasional checks
3. Look for signs the detector is updated against current LLMs, not just older models
4. Consider whether related tools — image detection, humanize/rewrite — are bundled in the same app
5. Read a handful of independent reviews rather than relying on a vendor's own accuracy claim