How to Use Grammarly AI Detection: A Practical Walkthrough

Why Teams Are Running AI Detection on Their Writing

Open a document today and there's a decent chance some of it was drafted with AI help — a first pass from ChatGPT, a polished paragraph from Gemini, a rewrite suggested by an assistant inside your editor. That shift is exactly why editors, managers, and educators are scrambling for a reliable way to gauge how much of a text is genuinely human-written.

Grammarly entered this space in August 2024, releasing its AI content detector despite having previously stated that AI detection tools are "unreliable," according to a competitor review. The move makes sense commercially: Grammarly is now identified as a "star player" in the AI detector market alongside Turnitin and Pindrop, given its strong market share and product footprint.

And the market is growing fast. Estimates vary by research firm — one puts the market at $581.3 million in 2025, heading toward $5.2 billion by 2033 — but every forecast agrees on strong double-digit growth. Education and academic integrity remain the dominant use cases, holding the largest share of demand in 2025.

Just as notable is how detection is being delivered. Platform-integrated detection is becoming the default, with platform-based solutions holding a 65.5% share in 2025. Rather than standalone tools, detectors are increasingly embedded as API/SDK components inside CMS, LMS, and collaboration platforms, supporting real-time verification where writing actually happens. For teams, this means detection is no longer a separate step — it's arriving inside the tools they already use.

Why does this matter for your editing process? Because AI-assisted writing is now routine, and detection scores are becoming a normal part of review workflows. That creates real risk: detection research shows most competing detectors fail to operate when forced to keep false positives under 1%, and content by non-native English speakers is more likely to be misclassified as AI-generated. A false accusation of AI use can damage reputations and working relationships far more than a missed detection.

Before relying on any detector, keep these realities in mind:

• Detection scores are probabilistic estimates, not verdicts — treat them as one data point among several.
• Accuracy claims conflict sharply: Grammarly reports 99% accuracy on the RAID benchmark, while a competitor's testing of the same benchmark type reported 22.22%.
• Detection works best on complete, substantial drafts — short, formulaic, or bullet-point text produces unreliable results.
• Layer detection with plagiarism checkers and human review rather than trusting any single tool.

For teams building AI into their writing workflows, the surrounding process matters as much as the tool itself. Agent skills can automate the verification steps around detection — routing flagged drafts, logging review outcomes, and cross-checking sources. SkillzBench is building a free directory of tested agent skills plus a free MCP connector, so you can equip your agents with exactly the workflow pieces you choose. Join the waitlist to be first in line when it launches.

How Grammarly's AI Detector Actually Works

Grammarly's AI detector doesn't work like a plagiarism checker scanning for copied phrases. Instead, it reads your document in sections, analyzes the language patterns within each one, and returns a single percentage score estimating how much of the text may have been AI-generated. The model behind it was trained on hundreds of thousands of human- and AI-written texts, and per Grammarly's own documentation, it evaluates content from ChatGPT, Gemini, Claude, and other major AI tools.

The free version of the detector is accessible without an account, though it imposes a word count limit per submission. Upgrade to Grammarly Pro and the workflow becomes more actionable: Pro users see exactly which sections were flagged and why, can revise flagged passages with a one-click AI Rewriter, and receive suggestions about where citations may be needed. That turns a raw score into an editing checklist you can actually work through.

Before you build detection into your editing process, though, you need to know about a serious contradiction in the accuracy claims. Grammarly states its detector ranked #1 for quality on the independent RAID benchmark with 99% accuracy. Originality.ai, a competitor, tested Grammarly on the same RAID dataset and reported 22.22% accuracy, an F1 score of 0.364, and concluded the tool is "easy to bypass with minimal effort" through paraphrasing and formatting changes. Both figures come from vendors with clear conflicts of interest, and no independent evaluation exists — so treat any accuracy claim, from any detector maker, with skepticism.

What does this mean practically for your editing workflow?

• Run detection on complete, substantial drafts rather than fragments — accuracy degrades on short text, bullet points, outlines, and formulaic content like templates.
• Treat the percentage score as "a guide—not a verdict," as Grammarly itself advises, and combine it with plagiarism checkers and human review.
• Cross-check high-stakes content with a second detector before making any consequential decision.
• Exercise extra caution with text by non-native English speakers, who multiple studies show are more likely to be misclassified as AI-generated.

That last point matters more than most people realize. In detection, a false positive — wrongly accusing a human writer — is often far more damaging than a missed AI passage, which is why analysis of false positives recommends against screening single sentences, math, or highly formulaic text at all.

One more consideration for teams: detection is increasingly moving out of standalone tools and into platforms. Platform-integrated solutions already hold the dominant market share, and API-based detectors that verify content in real time inside CMS, LMS, and collaboration tools are a major growth area. If you're assembling an editing or verification pipeline around tools like this, agent skills can automate the surrounding steps — SkillzBench is building a free directory of vetted agent skills plus a free MCP connector, so you can browse and test what fits your workflow.

A Step-by-Step Workflow for Detection-Driven Editing

A detection score only becomes useful when it sits inside a disciplined editing workflow — run the check, read the flags, revise deliberately, and verify with a second layer of evidence. Grammarly's own guidance frames it best: treat scores as "a guide — not a verdict" for decisions about originality and attribution, according to Grammarly's explainer on how AI detectors work.

Start by running detection on complete, substantial drafts. Grammarly's detector analyzes documents in sections and returns a percentage estimate, and reliability drops sharply on short or formulaic text. Pangram's guidance on false positives specifically warns against screening bullet-point lists, outlines, single sentences, and highly templated writing such as instruction manuals — exactly the material where detectors misfire most.

When results come back, don't stop at the headline percentage. Grammarly Pro shows which sections were flagged and why, which turns a vague score into actionable feedback. From there:

• Review each flagged section and the explanation provided before changing anything.
• Use the one-click AI Rewriter on passages that genuinely need revision, rather than rewriting the whole draft blindly.
• Identify where citations may be needed — flagged passages often signal missing attribution, not AI authorship.
• Cross-check high-stakes content with a second detector and human review, since accuracy claims conflict sharply between vendors.

That last point matters because the numbers disagree wildly. Grammarly claims 99% accuracy and a #1 quality ranking on the RAID benchmark, yet a competitor review by Originality.ai reports 22.22% accuracy under adversarial testing of the same benchmark, calling the tool easy to bypass. With stakes that contested, no single score should ever drive a consequential decision — and be especially careful with text by non-native English speakers, whom multiple studies show are misclassified more often.

Layer in complementary checks. Grammarly itself recommends pairing AI detection with plagiarism checking and Grammarly Authorship, which tracks what was typed by a human, generated with AI, or pasted and edited. Plagiarism checkers are more definitive than detectors but miss paraphrased copying, so the two together cover each other's blind spots.

Finally, treat the whole verification loop as a workflow you can automate. Agent skills can handle the surrounding steps — flagging drafts, logging verification results, routing flagged sections for human review — and SkillzBench is building a free directory where you can browse and test those skills, with every one benchmarked and security-scanned before you rely on it.

Run the check, read the flags, revise with intent, and verify twice. The score is one data point; your judgment is the workflow.

Avoiding False Positives and Overreliance

A false accusation of AI writing can damage a reputation far more than a missed detection ever could. That asymmetry is why the biggest risk in using Grammarly's AI detector isn't a low accuracy score — it's overtrusting the score itself.

The evidence for caution is real. Multiple studies show that content written by non-native English speakers is more likely to be misclassified as AI-generated, according to aggregated industry research. Grammarly's own blog acknowledges that detectors show bias against creative writers, developing student writers, and English-as-additional-language writers. If your team includes international colleagues or ESL writers, treat any flag on their work as a prompt for conversation, not a conclusion.

Accuracy claims also deserve skepticism. Grammarly claims 99% accuracy and a #1 quality ranking on the RAID benchmark — but a competitor review reports just 22.22% accuracy on the same dataset under adversarial testing. Both parties have conflicting interests, and the gap between those numbers is exactly why no consequential decision should rest on a single tool's percentage.

Accuracy also degrades on the wrong kind of input. Detection works best on complete, substantial drafts — not fragments. As a rule of thumb, skip screening entirely for:

• Short bullet-point lists, outlines, and single sentences
• Formulaic text like templates, spreadsheets, and instruction manuals
• Anything under a few hundred words where patterns are too thin to judge

Per detection-industry analysis, most detectors "fail to operate when forced to have a false positive rate under 1 percent" — meaning even the best tools trade false positives for catch rates.

So what does responsible use look like in practice?

Layer your verification. Grammarly itself recommends combining AI detection with plagiarism checkers, citation verification, and Authorship (which tracks what was typed, generated, or pasted). Plagiarism checkers are more definitive but miss paraphrased content — use both.

Cross-check high-stakes content. For anything consequential — a client deliverable, an academic submission, a published article — run it through a second detector and add human review. Market guidance increasingly advises against using detection scores as the sole measure of authenticity; treat them as supporting indicators alongside context and intent.

Be transparent about detection's role. Tell your team, students, or contributors that scores are probabilistic estimates, not verdicts, and explain how flags feed into your editing process. Transparency prevents the silent penalty problem, where someone's work gets quietly rejected over a number they never got to contest.

One practical note for teams building detection into their editing workflow: the surrounding steps — routing flagged drafts to reviewers, logging scores, scheduling cross-checks — are exactly the kind of repetitive work agent skills can automate. SkillzBench is building a free directory of verified agent skills plus a free MCP connector, so you can browse, test, and equip the skills that fit your process. Every skill is benchmarked and security-scanned, because the same skepticism you apply to a 99% accuracy claim should apply to the tools you plug into your workflow.

Automating the Rest of Your Verification Workflow

A detection score sitting in isolation tells you very little. Grammarly itself advises treating results as "a guide—not a verdict" and layering them with plagiarism checkers, citation verification, and human review — which means the real work happens in the steps around the detector, not inside it.

That surrounding workflow is where teams lose the most time. A typical AI-assisted editing pass involves drafting, revision of flagged sections, citation checks, plagiarism screening, and a final content QA review. Grammarly Pro condenses part of this — flagged passages come with explanations, a one-click AI Rewriter, and citation suggestions — but the rest still demands judgment and repetition across every document your team produces.

The good news: most of these steps follow a repeatable pattern. For example, a solid verification pass might include:

• Running detection only on complete, substantial drafts — never outlines, bullet lists, or formulaic templates, where accuracy degrades and false positives spike
• Cross-checking high-stakes text with a second detector and human review, given how sharply vendor accuracy claims conflict
• Verifying citations and originality with plagiarism tools, since detectors are probabilistic while plagiarism checkers are more definitive
• Applying extra caution to text by non-native English writers, who studies show are more likely to be misclassified as AI-generated

This is exactly the kind of structured, repetitive work that agent skills are built to automate. Agent skills are instruction packages (SKILL.md files) that plug into tools like Claude Code, Cursor, and Gemini CLI, letting an agent handle defined steps — a citation check, a revision pass, a QA checklist — consistently, every time. Instead of remembering to cross-check every draft manually, you encode the workflow once and let the agent execute it.

The catch is quality. Published research found that roughly 26% of agent skills carry at least one vulnerability, so grabbing skills blindly from GitHub is its own risk. That's the problem SkillzBench is building for: a free, independent directory where skills are version-pinned, security-scanned, license-checked, and benchmarked in a sandbox to measure how much each one actually improves agent performance. A free MCP connector will let you toggle exactly which skills any agent can see, and everything — the directory, the connector, the benchmarks — will be free to use. The waitlist is live now, so you can get early access and test skills before relying on them in your editing stack.

One data point from a detector, plus automated checks around it, plus human judgment on top — that's a verification workflow you can actually trust at scale.