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If an AI-assisted article sounds interchangeable, do not begin by swapping random synonyms. Measure the wording that repeats, compare it with strong human writing in the same subject and format, then rewrite the recurring phrases and sentence frames while preserving the facts. N-gram analysis—counts of one-, two-, and three-token sequences—shows where a draft has unusually concentrated language. A second pass should combine those counts with a manual check of sentence structure, specificity, accuracy, and reader value.
This method can make a draft more distinctive and useful, but it cannot prove who wrote it or guarantee that any detector will approve it. Topic, genre, translation, formal style, model family, and ordinary editing all change the statistical signals.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchWhat does it mean when an AI detector calls writing “templated”?
“Templated” usually describes a pattern of language that appears too predictable or too similar to other generated passages. The problem is not one forbidden word. It is concentration: the same short phrases, paragraph openings, transitions, and grammatical shapes appear again and again, so separate paragraphs begin to sound interchangeable.
A detector may combine many signals, and its label is not an authorship finding. A technical explainer, a translated article, or a deliberately formal report can look statistically regular even when a person wrote every sentence. Treat the warning as an editing prompt: identify the repeated patterns, decide whether they reduce clarity or originality, and improve the draft for readers rather than trying to “beat” a score.
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Why repeated wording feels mechanical
Readers process common phrases quickly. If every section begins with “In today’s digital landscape,” every contrast uses “not only … but also …,” and every paragraph ends with a broad promise, the prose develops a recognizable cadence. Changing the nouns does not change that cadence. The underlying frame remains the same.
Some repetition is useful. Product names, legal terms, measurements, and a key concept should recur so readers do not lose the thread. The goal is not zero overlap; it is purposeful repetition surrounded by enough specific information and varied structure to show real reasoning.
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What is n-gram math, and what can it reveal?
An n-gram is a contiguous sequence of n tokens. A token can be a word or another unit chosen by the counting method. Unigrams contain one token, bigrams contain two, and trigrams contain three. Counting them gives you a transparent view of which small units dominate a draft.
| Unit | Example from “check the facts before publishing” | What it helps reveal |
|---|---|---|
| Unigram | “facts” | Overused individual words, including vague verbs or repeated transition terms |
| Bigram | “the facts” | Stock collocations and repeated two-word transitions |
| Trigram | “check the facts” | Longer recurring phrases and paragraph-level habits |
For each unit, count occurrences and divide by the number of units in the document (or by a comparable corpus size). You can then compare the draft’s rates with a reference set of human-written pieces on the same subject, for the same audience and in the same format. A phrase that is common everywhere is less diagnostic than one that is unusually concentrated in your draft.
Microsoft’s explanations of BLEU, ROUGE, and Jensen–Shannon (JS) divergence show how n-gram overlap, frequency, and distributional divergence can be used to evaluate text. Those metrics were designed for tasks such as machine-translation or summarization evaluation, not as universal authorship tests. They are useful conceptual tools for an editor: measure overlap and distribution, then inspect the actual sentences.
A simple concentration measure
Let count_d(g) be the number of times a gram g appears in your draft and total_d(n) be the total number of n-grams of that size. The draft rate is:
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rate_d(g) = count_d(g) / total_d(n)For a reference corpus, calculate the same rate for each article and compare the draft with the corpus distribution. You can flag a gram when it is far above the reference median or appears in many draft paragraphs. There is no universal threshold: a cooking term, a legal phrase, and a generic transition have very different legitimate frequencies.
Why can an AI-assisted draft look templated even after editing?
Language models learn regularities from large collections of text and often produce safe, high-probability continuations. Light editing may change a few nouns while leaving the high-probability scaffolding intact. Three related effects matter.
Lexical repetition
Repeated bigrams and trigrams create verbal “seams.” Examples include the same transition in every section, a recurring “this means that” construction, or a fixed closing phrase. A frequency table makes these patterns visible even when a casual read does not.
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Syntactic repetition
Two sentences can use different words but share the same grammatical frame. Shaib, Elazar, Li, and Wallace reported at EMNLP 2024 that 76% of templates in model-generated text were found in pre-training data, compared with 35% of templates in human-authored text. Their work defines templates from part-of-speech sequences and reports that template features can distinguish models, tasks, and domains. The result does not mean every repeated frame is artificial; it explains why changing vocabulary alone may not change the perceived cadence.
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Model-specific signals
Signals are not stable across all generators. A 2026 Graphite analysis of 10,000 human articles and 90,000 AI-generated articles from nine models reported that many tells were specific to a model family and shifted between model versions. A detector trained on one distribution can therefore behave differently after a model update, a new prompt, or substantial human editing.
These findings support diagnosis, not a verdict. A signal can be affected by genre, subject vocabulary, translation, sentence length, and the quality of the comparison set. Human review remains necessary.
How do I build a fair reference set?
Use several strong human-written pieces that match your draft as closely as possible. The closer the comparison, the less likely you are to mistake normal genre language for a machine signal.
- Match the subject. Compare a cloud-security explainer with other cloud-security explainers, not with travel diaries.
- Match the audience. Consumer guidance, developer documentation, and academic prose use different vocabularies and sentence lengths.
- Match the format. Lists, FAQs, news reports, and opinion columns have distinct structural habits.
- Use recent, edited work. Older pages may contain obsolete terminology or a different house style.
- Keep the sources independent. Several pages copied from one template will make that template look “human” simply because it is common in the set.
Record the reference articles’ word counts and calculate rates rather than comparing raw counts. Keep your normalization rules identical for the draft and every reference. If your draft is 1,200 words and a reference is 8,000, raw occurrences are not comparable.
How do I run an n-gram check on my draft?
The following workflow is deliberately editorial. It combines transparent counting with a close reading, rather than treating a single score as a decision.
1. Normalize consistently
Make a copy of the draft. Convert text to a consistent case, normalize curly and straight quotation marks, and decide how to handle hyphens, apostrophes, numbers, URLs, and headings. Apply the exact same rules to the reference set. Keep the original copy so you can preserve intentional capitalization, product names, and code.
Do not strip every punctuation mark automatically. Punctuation can help you inspect sentence frames, and removing it may join words that were never adjacent in the prose.
2. Count unigrams, bigrams, and trigrams
A short Python script using the standard library is enough for a first pass:
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import re
from collections import Counter
TOKEN = re.compile(r"[A-Za-z0-9]+(?:['’-][A-Za-z0-9]+)?")
def grams(text, n):
tokens = [t.lower() for t in TOKEN.findall(text)]
return Counter(tuple(tokens[i:i+n]) for i in range(len(tokens)-n+1))
def top_rows(text, n, limit=25):
counts = grams(text, n)
total = sum(counts.values())
return [
(" ".join(g), c, c / total if total else 0)
for g, c in counts.most_common(limit)
]
for n in (1, 2, 3):
print(f"Top {n}-grams")
for phrase, count, rate in top_rows(open("draft.txt", encoding="utf-8").read(), n):
print(f"{phrase}t{count}t{rate:.4%}")This script is a diagnostic, not a detector. It treats hyphenated forms and apostrophes according to the regular expression shown; change the rule only if you change it for every comparison document. Remove boilerplate such as navigation menus before counting, or that site-wide text will dominate the results.
3. Compare rates with the reference corpus
For each gram, calculate its rate in every reference article. Look for phrases that are unusually high in the draft and appear across multiple paragraphs. A phrase used three times in a 600-word article may deserve attention; the same phrase in a 20,000-word legal document may be ordinary. Use a spreadsheet or a small script to sort by the difference between the draft rate and the reference median.
4. Map each flag back to its sentences
Frequency alone cannot tell you whether a phrase is a problem. Search the draft for every flagged gram and mark:
- Repeated paragraph openings or identical transition slots.
- Sentences with the same subject–verb–object order and the same clause lengths.
- Generic claims that could be moved to any topic without changing their truth.
- Clusters of “is not X; it is Y,” “whether you are X or Y,” or other contrast frames.
- Headings that follow a rigid sequence even when the underlying questions differ.
Keep legitimate repetition: a defined term, a warning label, a command, or a key measurement should remain consistent. Flag a unit because it contributes to a pattern, not because it is on a forbidden-word list.
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Replace each word in a sentence with a coarse part-of-speech tag—noun, verb, adjective, adverb, determiner, and so on—and compare the resulting sequences. For example, “The tool checks every file” and “The script scans each document” share a determiner–noun–verb–determiner–noun frame even though their vocabulary differs.
You can do this manually for a short article: underline the subject, circle the main verb, and mark introductory clauses. For a longer project, a part-of-speech tagger can produce sequences for comparison. Treat the output as a lead, because tagging can be wrong with product names, abbreviations, and technical terms.
Frames worth varying when they add no clarity
- Every section opening with an abstract “In this guide…” announcement.
- Every paragraph using a three-part list with the same rhythm.
- Repeated “not only … but also …” contrasts where a direct statement would be clearer.
- Identical sentence endings such as “for better results” or “in today’s digital world.”
- Repeated passive constructions when the responsible actor is known.
Do not vary a frame merely to create noise. A consistent procedure, warning, or definition may be easier to follow when it repeats. Change the structure when the repetition hides the logic or makes separate ideas sound mass-produced.
What should I change after the analysis?
Rewrite the information and voice, not just the surface vocabulary. Work from the reader’s question and the evidence you can stand behind.
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Use genuine perspective
Explain why you made a recommendation, what trade-off it addresses, or which failure mode a reader should expect. Do not manufacture first-hand testing, customer stories, or personal experience. A transparent explanation of reasoning is more valuable than a simulated anecdote.
Vary sentence openings for logic
Let the subject of each sentence follow the argument: a limitation, a user action, a result, or a condition. Avoid replacing every “however” with a synonym; sometimes deleting the transition is the cleanest fix.
Break interchangeable paragraphs
Give each paragraph one job. A paragraph that defines a term should not also introduce a recommendation and repeat the conclusion. Move examples next to the claim they clarify, and use a list or table when the reader is comparing items.
Preserve terminology and facts
Do not alter a technical term, command, legal wording, or measurement merely to lower overlap. Check every changed sentence against the underlying documentation. A less predictable article that contains an error is worse than a regular-sounding article that is correct.
How can I re-measure the rewrite without damaging it?
Run the same normalization and counting process after editing, then read both versions aloud or have another editor review them. Look for lower concentration in the flagged areas, but also check whether clarity, search intent, and factual precision improved.
| Review axis | Question to ask | Acceptable outcome |
|---|---|---|
| Lexical repetition | Are a few generic bigrams or trigrams dominating? | Unnecessary clusters shrink; essential terms remain consistent. |
| Syntactic repetition | Do paragraphs still use the same openings and frames? | Structure follows the argument rather than a fixed cadence. |
| Reference similarity | Is the draft unusually close to comparable human writing? | Shared terminology is explainable by the topic; distinctive phrasing carries the explanation. |
| Robustness | Does the pattern disappear after ordinary human editing? | Useful observations survive small edits and are not dependent on a single detector. |
| Editorial quality | Is every claim accurate, relevant, and useful? | The reader can act on the information and understand its limits. |
Google’s guidance for automatically generated content puts accuracy, quality, and relevance first and recommends giving readers context about how automation was used. Those goals are a better finish line than a particular detector percentage.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can n-gram statistics prove that a person or a model wrote the text?
No. N-gram concentration measures surface distribution and similarity; it does not establish authorship, intent, or legal ownership. Human writers can share common phrases, and models can produce unusual wording after prompting or editing.
Provenance systems are a different category. OpenAI has described textGrain as a signal that subtly changes the randomness of word or word-piece choices. Such a signal is not the same as n-gram analysis, and provenance signals do not guarantee accuracy, identify legal ownership, prove how much a model contributed, or remain available after every transformation.
Use detector output as one uncertain indicator. If a publishing, academic, or workplace policy requires disclosure, follow that policy and retain your drafts, notes, and sources so you can explain how the work was produced.
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Which mistakes make the “fix” worse?
- Random synonym replacement: It can create awkward collocations, alter technical meaning, and leave the sentence frame untouched.
- Chasing a single score: Detector versions and model-specific signals change, so an apparent improvement may not generalize.
- Comparing unlike genres: A formal manual naturally shares patterns with other manuals; that is not evidence of generation.
- Deleting necessary repetition: Readers need consistent names for the same setting, concept, or command.
- Adding invented detail: Specificity helps only when it is true and supportable.
- Ignoring translation and accessibility: A writer using a second language, a style guide, or assistive technology may produce regular patterns for legitimate reasons.
- Removing all transitions: Choppy prose is not more original. Use transitions when they show a real relationship between ideas.
- Failing to fact-check after rewriting: A changed qualifier can turn a limited claim into a false universal.
When should I disclose AI assistance?
Disclosure depends on the publication, employer, school, or client policy. When no rule specifies a format, a short, accurate note can explain whether a model helped brainstorm, outline, edit, translate, or generate an initial draft. Do not claim that a person wrote every word if that is not true, and do not imply that a disclosure makes unverified claims acceptable.
Keep the disclosure proportional to the work. A spell-checking pass and a generated first draft are materially different uses. Readers mainly need enough context to understand the process and evaluate the article’s accountability.
What is the practical decision rule?
If the n-gram report shows a concentration that also sounds repetitive to a human reader, rewrite the affected passages for specificity, structure, and voice, then fact-check them. If the report flags phrases that are necessary terminology and the prose is clear, keep them and document why. If a detector flags the article but neither the counts nor the close reading reveal a meaningful problem, do not distort accurate writing to satisfy an opaque score.
The durable fix is editorial: make every paragraph earn its place, make every claim appropriately precise, and let sentence structure reflect the reasoning. N-gram math is valuable because it turns a vague “this feels generated” reaction into a set of patterns you can inspect and improve.
Frequently Asked Questions
Will changing every repeated phrase guarantee a human score?
No. Detector behavior varies by model, version, topic, and editing history. Removing useful terminology can hurt readability without changing the underlying signal, so judge the revision by accuracy and reader value first.
How large should my reference corpus be?
There is no universal minimum. Start with multiple independent pieces that match the draft’s subject, audience, and format, and expand the set if one author or one house style appears to dominate the frequency profile.
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They can surface overlapping wording, but overlap is not a plagiarism finding. Establishing copying requires source comparison, context, dates, permissions, and the applicable policy or law.
What if English is not my first language?
Regular phrasing may reflect translation or a learned style rather than model use. Compare the draft with writing by people using the same language background and prioritize clarity, factual accuracy, and an honest account of editing assistance.
Should I keep my drafts and prompts?
Keeping version history, notes, sources, and any material model outputs can help you answer questions about your process. Store only what your organization’s privacy and retention rules allow.
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