AI Content Detector

Check if your content was likely written by a human or generated by AI.

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What is an AI Content Detector?

This page analyzes writing patterns that often differ between human drafts and machine-generated text. It looks for vocabulary variety, sentence rhythm, repeated phrasing, and boilerplate disclaimers that appear in templated prose.

Why Does AI Detection Matter?

Pro Tips

How to Use the AI Content Detector

  1. Paste your text into the box above.
  2. Click Detect to run the analysis.
  3. Review the verdict and the heuristic metrics table.
  4. Revise: reduce repetition, add examples, and adjust rhythm where needed.

How to interpret the metrics (with examples)

Signals that often read as human

  • Type–Token Ratio (TTR) sits around 0.25–0.45 with domain-specific nouns and verbs.
  • Sentence length variance shows a mix of short punchy lines and longer explanations.
  • Top-word share stays low (<5%) once stopwords are removed.
  • Concrete details (dates, numbers, names, sources) appear throughout.

Patterns that can look machine-generated

  • Uniform pacing where nearly every sentence is the same length.
  • Repeated phrasing that loops the same term each paragraph.
  • Stock transitions (formulaic openers and stacked connectors) used as filler.
  • Vague summaries with broad claims and few verifiable specifics.
Rule of thumb: weigh signals together. No single metric proves authorship.

How the detector works (methodology)

We use quick, explainable checks rather than a black-box classifier. The goal is to highlight patterns you can read and improve.

Weights are heuristic and tuned so a single trigger doesn’t dominate the overall score.

Troubleshooting & fine-tuning your text

Grading & compliance guidelines

Important: treat these results as indicators. For academic, HR, legal, or publishing contexts:

  1. Compare drafts or version history to confirm a natural revision trail.
  2. Request a brief oral explanation or a small targeted revision.
  3. Verify citations and facts; watch for placeholders or invented sources.
  4. Keep an audit note (date, context, what you checked) for consistency.
  5. Apply one documented policy and share it with participants.

We do not store pasted text. See the privacy note below.

Mini glossary

Before/after: quick humanization fixes

Before (generic & light on detail)

“To sum up, site speed matters. Also, websites should think about Core Web Vitals.”

After (adds specifics & rhythm)

“Page speed affects conversions. In our April crawl, the product page LCP averaged 4.6s on mobile; target ≤2.5s. Compress the 900-KB hero, defer the carousel script, and lazy-load review images. Re-check Core Web Vitals after deploy.”

Tip: replace generic connectors with concrete actions, measurements, and dates.

FAQs

Is this 100% accurate?

No. It’s a heuristic detector. Treat results as signals, not proof.

Can I use this for grading?

Use caution. Combine the result with manual review and other evidence.

What improves “human-likeness”?

Concrete details, personal experience, varied sentence lengths, and specific nouns/verbs increase uniqueness.

Is my text stored?

No. This on-page tool analyzes the text server-side for your current request only.

Does formatting (bullets, headings) affect detection?

Only indirectly. Signals are text-based; good structure can improve clarity but doesn’t “game” the metrics.