How to Check Whether Text Was Generated by AI

How to check whether text was written by AI: a reliable method and how to interpret results (updated April 2026)

If you want to check whether text was written by AI, start by defining what it means to "prove" where content originates and what you are actually trying to protect.

This guide builds on the principles set out in our AI detection article and focuses specifically on text-level checks: how to read scores, a practical control protocol, how to reduce errors, and the implications for SEO + GEO.

This guide complements the "AI detection" article: what you gain by going deeper on text-level checks

Text-level verification is primarily about making operational decisions you can stand behind: publish, revise, request sources, or escalate to specialist human review.

In B2B, this becomes critical because the impact is not just reputational: it affects compliance, editorial accountability, credibility with decision-makers, and acquisition performance.

Context: AI use is becoming mainstream. In 2026, ChatGPT reportedly has 900 million weekly users (Backlinko, 2026) and 75% of employees use AI at work (Microsoft, 2025).

As AI becomes ubiquitous, checking whether text is AI-generated needs to go beyond style and incorporate evidence (sources, consistency, traceability) rather than relying on a simplistic verdict.

What a check can confirm (and what it should not conclude) to avoid bad decisions

A check can provide a probability that text was generated, flag "atypical" sections, and surface risks (repetition, stock phrasing, inconsistencies).

However, it should not be treated as definitive proof of intent, wrongdoing, or non-compliance: detectors do not have access to the true writing history.

Text can be human yet "look like AI" (standardised style, heavy jargon, translation), or AI-assisted yet "look human" (rewriting, personalisation, specific data).

The right stance is: score + context + additional checks.

Clarify the goal of the check: compliance, quality, SEO and GEO

Before opening a detector, set the objective. The same text may be acceptable for marketing, risky for regulated communications, and counterproductive for SEO if it does not deliver genuine value.

Think of verification as governance: reduce risk, improve reliability, and protect performance across both Google and generative engines.

B2B use cases: marketing content, documentation, regulated content, multi-author content

In B2B, the most common scenarios combine volume with high standards: acquisition pages, white papers, help centres, knowledge bases, sales collateral, or multi-country content.

The level of control should match the risk. Regulated content (HR, finance, healthcare) requires stricter factual and legal checks than a blog post.

• Marketing: check accuracy, differentiation, the offer, and supporting evidence.
• Documentation: check correctness, prerequisites, and reproducibility.
• Regulated: check compliance, disclaimers, and sources.
• Multi-author: check tone alignment, cross-page consistency, and traceability.

SEO vs GEO: why being "detectable" is not the real issue, and what you should actually secure

In SEO, Google remains dominant (89.9% global market share; Webnyxt, 2026), but formats are shifting with generative answers and increasingly "no-click" interfaces.

In 2025, 60% of searches were reportedly zero-click (Semrush, 2025): visibility is no longer just your ranking, but also your ability to be summarised or cited.

So trying to make text "undetectable" is the wrong goal. What you need to secure is usefulness, accuracy, structure, evidence, and quotability (GEO).

Helpful, people-first content remains the reference point, regardless of how it is produced, in line with Google's public statements via Danny Sullivan (Search Liaison, 2022–2023).

How AI text checking works: detection principles and signals analysed by a detector

An AI detector for text most often relies on statistical and linguistic analysis: it compares your text against patterns observed in human corpora and generated corpora.

The output is a "risk" or a "probability", not hard evidence.

A probabilistic approach: score, highlighted segments and confidence level

Tools typically provide an overall score and highlight segments deemed "highly predictable" or overly regular.

Treat this score as a triage signal. Longer texts allow more stable aggregation of signals, but that still does not guarantee a correct conclusion.

If the tool provides a confidence level or paragraph-level detail, use it to focus human review: those are the sections to audit first.

Recommended decision rule: never rely on a single score without complementary checks (sources, plagiarism, coherence).

Common linguistic signals: regularity, redundancy, transitions and vocabulary distribution

Without getting into reverse engineering, most detectors assess signals such as consistent sentence patterns, overly smooth transitions, or semantic repetition.

They may also react to overly uniform vocabulary, repeated generic definitions, or stock connectors that do not reflect a real argument.

• Regularity: similar sentence length and an unnaturally steady rhythm.
• Redundancy: rephrasing that adds no new information.
• Transitions: automatic-sounding connectors with weak argumentative logic.
• Vocabulary: generic terms and a lack of domain-specific perspective.

Why copy-paste, translation and rewriting can distort results

Three operations can strongly disrupt signals: copy-pasting heterogeneous fragments, machine translation, and paraphrase-style rewriting.

Translation can standardise style and create artificial "regularity", even when the original content was human-written.

Mechanical rewriting (synonyms, sentence inversion) may reduce certain signals while lowering quality, which is counterproductive for SEO.

Finally, stitching together multiple sources can create tone breaks and inconsistencies that a tool may misread in either direction.

Reliability, limitations and risks: reading results without over-interpreting

The reliability of checking depends more on your context (text type, language, format, constraints) than on a tool's marketing claims.

A score does not replace editorial validation, especially when legal, reputational or commercial stakes are involved.

What reduces detection reliability: short texts, heavy jargon, highly standardised style, languages, quotes and lists

Short texts provide too little statistical material. Highly standardised writing (procedures, terms and conditions, technical sheets) naturally looks "regular".

Domain jargon and acronyms can skew models, particularly if the detector's training data under-represents your industry.

Quotes, tables and lists can also distort signals because they interrupt stylistic continuity without indicating origin.

Recommendation: segment the text, analyse by block, and compare against human texts of the same internal format.

Understanding errors: false positives, false negatives and bias by content type

A false positive flags a human text as "probably AI". A false negative lets generated text pass.

In practice, both happen, and frequency varies by language, topic and the level of human editing.

The most common enterprise bias is confusing "clean corporate style" with "AI style". The stricter your editorial guidelines, the higher the risk of false positives.

Operational conclusion: treat the detector as a filter, not a judge.

False positive rate: what it means, how to measure it, and why it varies by corpus

The "false positive rate" is the share of human texts the tool incorrectly classifies as generated.

You cannot estimate it seriously without a reference corpus: texts of known origin covering your formats (pages, emails, docs) and languages.

It varies because detector training corpora often do not match your reality (brand tone, sector constraints, templates).

Measure it internally, then calibrate decision thresholds by content type (rather than a single universal threshold).

Implement evidence + context validation rather than automated verdicts

Adopt a two-input validation model: evidence (sources, traceability, coherence) and context (risk, audience, use case).

An operational step-by-step process to check text

To make checking repeatable, formalise a process. You reduce arbitrary decisions, internal debates, and expensive mistakes.

Goal: a protocol that is simple to execute, yet robust enough to handle edge cases.

Step 1: define the scope (goal, audience, risk level, legal constraints)

Start with a short scoping sheet. It sets the "why" and prevents you applying the same standards to an internal email and a page that represents the brand publicly.

• Content objective (conversion, information, compliance).
• Audience (prospects, customers, regulator, internal).
• Risk level (low, medium, high).
• Legal constraints and required validations.

Step 2: prepare the text (source version, quotes, references, reused sections)

Lock a "source" version (versioning) before analysing. Otherwise, you end up comparing variants and lose traceability.

Identify quotes, legal excerpts, standard definitions and reused blocks. Exclude or tag them because they often skew scores.

If the text is translated, keep the original and note the translation method used.

The aim is reproducible, audit-friendly analysis.

Step 3: cross-check signals (detection, plagiarism, sources, factual coherence)

Do not stop at AI detection. Cross-check signals, especially if you publish at scale.

1. Run a detector (overall score + risk areas).
2. Run a similarity check using an anti-plagiarism software that fits your constraints.
3. Verify sources (links, dates, internal documents, quotations).
4. Check coherence (figures, definitions, internal contradictions).

Step 4: decide (accept, revise, request writing evidence, rework)

Decide using a simple matrix based on risk and observed quality.

Step 5: document (traceability, versioning, validation criteria, internal audit)

Document what was done: version analysed, tools used, date, thresholds, fixes applied, sources.

This traceability protects the team if decisions are challenged and helps you improve calibration over time (reducing false positives).

In B2B, documentation also supports quality across multiple authors and countries.

Keep it short, but make it systematic.

Choosing detection tools: practical criteria and an evaluation grid

Your tool choice should follow real constraints: languages, confidentiality, export needs, integrations, and the ability to calibrate.

In practice, the value is less about a "magic score" and more about being able to industrialise reliable checks.

Essential criteria: methodological transparency, data handling, languages, exports and API

For B2B use, demand verifiable criteria. Without transparency, you will not be able to defend decisions in an internal audit.

• Transparency: explain outputs (segments, confidence, limitations).
• Data: retention, training use, confidentiality, deletion.
• Languages: consistent performance across your markets.
• Exports: reports, evidence, verification logs.
• API: automation and workflow integration.

Tests to run before scaling: internal corpus, "known" texts, thresholds and calibration

Before rolling out broadly, test on a "known origin" internal corpus. Include your most templated formats and translated content.

1. Human sample (writers, experts, older publications).
2. AI sample (generated content, then edited, then translated).
3. Measure false positives by format (sheet, article, doc, email).
4. Define thresholds by risk (not a single threshold).

Goal: turn a "generic" tool into a protocol that fits your reality.

Organising quality control at scale: sampling, human review and workflows

At scale, verify by sampling, then increase scrutiny for high-risk content (sensitive topics, high visibility, strong claims).

Add human review for highlighted areas and for sections that carry brand liability (promises, figures, comparisons).

Your workflow should include clear gates: production → control → revision → approval → publication.

This structure prevents over-checking and protects production speed.

Reducing false positives: editorial best practice and control protocols

False positives are the most expensive internal mistake: they damage trust, slow teams down, and trigger unnecessary conflict.

You reduce them mainly through better method (calibration, segmentation, comparison) rather than switching tools.

Make diagnosis more robust: minimum length, segmentation and version comparison

Avoid evaluating very short texts. Prefer segmented analysis (introduction, each H2 section) on sufficient volume.

Compare the "before edit" and "after edit" versions. If the score shifts dramatically, that often signals style bias rather than certain origin.

• Segment by sections (H2/H3) rather than analysing the whole document as one block.
• Exclude quotes, standard excerpts and bibliographies.
• Compare against a human text of the same template (internal baseline).

This approach reduces gut-feel decisions.

Avoid "high-risk" styles: overly rigid templates, artificial synonym swapping and mechanical paraphrasing

The more rigid the template, the more it resembles automated production. This is especially true for multi-page content generated from identical patterns.

Artificial synonym swapping and mechanical paraphrasing often make things worse: they may lower some signals, but they make the text less natural and less useful.

For SEO, Google explicitly documents automatically generated content "without regard for quality" and content produced through paraphrasing/obfuscation as spam examples (Google documentation and public statements referenced in Incremys sources).

Priority: improve substance, not disguise form.

Factual checking: sources, quotations, verifiable data and intra-document coherence

Factual checking is your best defence because it does not depend on a detection model. It also strengthens GEO: generative engines prefer well-sourced, consistent and instructive passages.

• Add primary sources (documents, studies, official pages).
• Check dates, units, scope and definitions.
• Test intra-document coherence (same figure, same method, same assumption).

Example context: 51% of global web traffic is reportedly generated by bots and AI (Imperva, 2024). Citing the source and its scope prevents misinterpretation.

Improving AI-assisted text without "masking" it: quality, evidence and quotability (SEO + GEO)

Key question: how do you improve AI-assisted copy without trying to "game" detectors? The answer is straightforward: increase human-added value.

This aligns with SEO (usefulness) and GEO (passages that generative systems can reuse).

Increase usefulness: precision, domain examples, constraints, steps and counter-examples

High-performing content includes elements generic models rarely produce without a strong brief and data: decisions, constraints, trade-offs, and realistic examples.

• Specify the context (B2B, industry, risk level, audience).
• Add actionable steps (checklists, matrices).
• Include a counter-example (what not to do, and why).
• Introduce evidence (sources, definitions, scope).

In SEO, structure and depth matter: the average top 10 Google article is 1,447 words (Webnyxt, 2026), reflecting an expectation of more complete, more useful content.

Reduce generic phrasing: vague claims, superlatives and empty lists

Detectors often react to boilerplate passages but, more importantly, readers gain nothing from them.

Replace vague claims with criteria. Replace superlatives with evidence or conditions.

• To avoid: "revolutionary solution", "guaranteed results", "the ultimate method".
• To do instead: "in this context", "under this constraint", "measured via this KPI".

The outcome: a more credible text, better understood by Google, and easier to reuse in generative answers.

Structure for extractability: definitions, direct answers, tables, FAQs and sources

For GEO, think "extractable": short blocks, clean definitions, and direct answers to questions.

With high zero-click behaviour (Semrush, 2025), your content should remain valuable even if users do not click immediately.

• Start some sections with a 1–2 sentence direct answer.
• Add decision tables (thresholds, actions, risks).
• End with an FAQ that covers real objections.

And whenever you use figures, always include the source and the year.

Measuring SEO and GEO impact after revision: what to track and how to decide

Checking only makes sense if it improves performance (or reduces risk) without harming SEO.

Measure before/after over comparable periods, and connect editorial changes to clear KPIs.

Google Search Console: impressions, CTR, rising queries and high-potential pages

In Google Search Console, track changes in impressions, CTR and long-tail queries. Searches of more than three words reportedly represent 70% of queries (SEO.com, 2026), which favours structured, precise content.

Watch high-potential pages: those with lots of impressions but low CTR, or that sit at the bottom of page one/top of page two (a highly elastic zone).

A useful benchmark: the number one organic result captures around 34% of clicks on desktop (SEO.com, 2026), so every ranking gain can be very profitable.

To put your KPIs into context, use the latest SEO statistics.

Google Analytics: engagement, conversions, entry quality and journeys

In Google Analytics, check whether revisions improve real engagement: reading time, scroll depth, navigation to business pages, and conversions.

Quality comes first: if the page attracts more traffic but lowers conversion rate, you may have optimised for the detector rather than for the user.

On mobile, technical performance matters too: 60% of global web traffic comes from mobile (Webnyxt, 2026), so longer content must remain readable and fast.

Always decide using a business indicator (lead, demo, purchase) alongside reading metrics.

GEO signals: brand consistency, reusable answers, quotable passages and question coverage

In GEO, look for quotable passages: clear definitions, steps, criteria, sourced figures and unambiguous wording.

An indirect indicator is how well you cover intent questions without drifting, and whether you provide reusable blocks.

Maintain brand consistency: overly neutral text loses differentiation, whilst overly templated text loses credibility.

Remember that visibility also happens off-click, through summaries and citations.

A word on Incremys: scaling verification and quality control without piling up tools

In practice, the hard part is not running a check, but integrating it into a multi-author production and approval system. Experience also shows that AI personalisation can make origin harder to distinguish, which means useful verification shifts towards compliance, coherence and evidence.

Incremys supports this SEO + GEO industrialisation approach: structuring briefs, production, validation and performance steering so quality control becomes a workflow rather than a set of isolated actions.

Build a data-driven editorial workflow: audit, production, validation and reporting (SEO + GEO)

A data-driven workflow connects three things: what you produce, what you validate, and what you measure (Search Console, Analytics, GEO signals).

The expected benefit: faster trade-offs, better-justified approvals, and more consistent quality at scale, without overloading teams.

Keep a simple principle: every published piece should be able to explain what it claims, which sources it relies on, and which intent it serves.

This level of governance protects performance in a web where automation is accelerating rapidly.

FAQ: how to check whether text is AI-generated

How do you check whether text is AI-generated?

Use a two-step approach: (1) run a detector to get a probabilistic signal and identify high-risk segments, then (2) validate with evidence: sources, coherence, traceability and a similarity check.

For a robust decision, segment the text (by section) and compare it with human content in the same format (your internal baseline).

Which tools should you use to check whether text is AI-generated?

At minimum, use a detector (for the signal) and a similarity/attribution tool (for reuse risk), then add factual checks (sources, dates, scope).

Your selection should reflect B2B constraints: data confidentiality, languages, exports, API access and the ability to calibrate thresholds by content type.

How reliable is checking whether text is AI-generated?

Reliability varies by context: it drops for short texts, highly standardised writing, translated content, and texts heavy with quotations or lists. It can also be skewed by domain jargon.

Treat the score as a triage indicator, not proof, and confirm with complementary checks.

Why do false positives happen when checking whether text is AI-generated?

Because some human writing styles statistically resemble generated text: strict editorial guidelines, repetitive templates, very corporate prose, translation, or technical documentation.

A detector's training corpora may not reflect your domain, creating bias.

How can you reduce false positives when checking whether text is AI-generated?

Segment the text, exclude quotations, enforce a minimum length, and compare against an internal baseline (known human texts).

Also calibrate thresholds by format (article, sheet, documentation) rather than relying on one single threshold.

What false positive rate is acceptable depending on the context (education, HR, SEO, compliance)?

There is no universally acceptable rate: it depends on the cost of being wrong. In compliance/HR, a false positive can have human and legal consequences, so you need a higher standard of evidence than a score.

The right approach is to measure your own false positive rate on an internal corpus and define decision rules by risk level.

Can AI-generated writing go unnoticed?

Yes, especially when content is edited, personalised and aligned with a brand voice. But "going unnoticed" is not a meaningful goal for SEO + GEO.

Your priority should remain quality, accuracy, differentiation and the ability to be cited with confidence.

How can you improve AI-generated text so it passes detectors?

Do not try to bypass detection. Improve value: add evidence, domain examples, actionable steps, counter-examples and sources.

Avoid mechanical paraphrasing: it can reduce usefulness and may align with patterns Google considers problematic when the goal is ranking rather than helping users.

What score threshold is genuinely usable?

A usable threshold is one you have calibrated on your own content and languages. Without internal calibration (known-origin texts), a generic threshold exposes you to false positives and false negatives.

Define thresholds by risk (low/medium/high) and by content type.

What should you do if a human text is flagged as AI-generated?

Do not conclude from the score alone. Re-run the protocol: segment the text, exclude quotations, compare with other texts by the same author and in the same format, then perform factual checks.

Document the incident and adjust thresholds if it recurs on similar content.

Which content types are hardest to assess (legal, technical, translation, summaries)?

Legal and technical content (standardised style), translations (style smoothing), and summaries (high density, regularity) are among the hardest to evaluate.

They require evidence + context validation and often expert human review.

How do you check content at scale (multi-author, multi-country) without harming quality?

Use sampling, set thresholds by language and format, and implement a clear workflow (production → control → revision → approval).

Centralise traceability: versioning, sources, reports and decisions. That is what keeps quality stable as volume increases.

How do you balance AI control, SEO and GEO without producing "templated" content?

Optimise for usefulness rather than stylistic compliance. Use structure for readability (headings, lists, tables), but enrich with domain angles, real constraints and evidence.

In the negative sense, "templated" content often lacks differentiation, which is exactly what harms SEO performance and GEO quotability.

What evidence and sources should you keep to justify the quality of published content?

Keep: the source version, date, contributors, sources used (links and documents), validations (expert, legal), and control reports (detection, similarity).

This documentation supports internal audits, updates and credibility.

To go further, find more practical guides on the Incremys blog.