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Get StartedAI content detection is the process of analyzing text to determine whether it was written by a human or generated by an artificial intelligence model like ChatGPT, Claude, Gemini, or similar large language models. As AI-generated content has become increasingly common across the web, the ability to distinguish between human and machine authorship has grown into a critical concern for publishers, educators, and SEO professionals alike.
Detection tools work by analyzing statistical patterns in text. AI models generate text by predicting the most probable next word in a sequence, which produces writing that is statistically "smoother" than typical human writing. Humans tend to write with more variation in sentence length, more diverse vocabulary choices, more unexpected word combinations, and less uniform paragraph structures. These measurable differences form the basis for most detection approaches.
Our AI content detector examines multiple statistical signals in your text to estimate the probability of AI authorship. Rather than relying on a single metric, it analyzes a combination of factors:
Each signal is weighted and combined into an overall AI probability score. The more signals that align with AI-typical patterns, the higher the confidence that the text was machine-generated.
Google has clarified that AI-generated content is not automatically penalized. Their focus is on content quality and helpfulness, regardless of how it was produced. However, the practical reality is more nuanced. Mass-produced AI content that adds no unique value, insight, or expertise is exactly the kind of content Google's helpful content system is designed to demote.
For SEO professionals, understanding whether content reads as AI-generated matters for several reasons. First, Google's quality raters are trained to identify low-effort content, and AI patterns can signal low effort even if the information is accurate. Second, users increasingly recognize AI-generated text and may trust it less, affecting engagement metrics like time on page and bounce rate. Third, competitors who invest in original, experience-backed content will outperform sites relying on unedited AI output over time.
The best approach is to use AI as a drafting tool, then heavily edit the output to inject personal expertise, real examples, original data, and a genuine human voice. This detector can help you identify which passages still read as machine-generated so you can revise them before publishing.
No AI detection tool is perfectly accurate, and it is important to understand the limitations. Detection accuracy decreases significantly with shorter text samples -- a single paragraph provides far fewer statistical patterns to analyze than a full article. Heavily edited AI text can also evade detection, as human editing introduces the natural variation that detectors look for.
False positives are a real concern. Some human writers naturally produce text that exhibits AI-like patterns -- technical writers, non-native English speakers, and writers who follow rigid style guides may all trigger false positives. Conversely, sophisticated AI models with temperature adjustments and style prompting can produce text that appears more human-like.
This tool is designed to provide useful signal, not absolute truth. Use it alongside your own editorial judgment, especially for high-stakes decisions. The statistical approach we use focuses on measurable text properties rather than trying to fingerprint specific AI models, which makes it more robust but also more probabilistic in its assessments.
This tool uses statistical heuristics to estimate AI probability. It is not 100% accurate -- no AI detector is. It analyzes patterns like sentence uniformity, vocabulary diversity, burstiness, and transition word usage. Use it as one data point, not a definitive judgment.
Google has stated that AI-generated content is not inherently penalized. However, content that is low-quality, unhelpful, or clearly mass-produced can trigger Google's helpful content system. The key is whether content provides genuine value to readers, regardless of how it was produced.
The detector analyzes multiple statistical signals including sentence length uniformity, vocabulary diversity (type-token ratio), burstiness (variation in sentence structure), transition word density, paragraph length consistency, sentence starter diversity, and average word length. AI text tends to be more uniform and predictable across these metrics.
We recommend at least 100 characters, but 300 or more words will give more reliable results. Short text samples have fewer statistical patterns to analyze, which reduces detection confidence. Longer passages allow the tool to identify more meaningful patterns.