AI Detection Glossary
Plain-English definitions of the key terms behind AI detection, generative AI, and content authenticity.
AI Detector
A tool that analyzes writing to estimate whether it was produced by a human or generated by an artificial intelligence model. GPT Zero detects AI text by measuring statistical patterns such as perplexity and burstiness rather than matching against a database.
AI Humanizer
Software that rewrites AI-generated text to read more like natural human writing, typically by increasing perplexity, adding sentence-length variation, and diversifying vocabulary while preserving the original meaning.
Burstiness
A measure of the variation in sentence length and structure across a passage. Human writers mix short and long sentences, producing high burstiness, while AI models tend to write with uniform rhythm, producing low burstiness.
ChatGPT
A conversational AI product from OpenAI built on the GPT family of large language models. It generates human-like text in response to prompts and is one of the most common sources of AI-generated content that detectors are asked to identify.
E-E-A-T
Experience, Expertise, Authoritativeness, and Trustworthiness — the quality signals Google uses to assess content. Pages that demonstrate genuine, human expertise tend to rank higher, which is why verifying authentic authorship matters for SEO.
False Positive
When an AI detector incorrectly flags genuinely human-written text as AI-generated. Minimizing false positives is critical in academic and professional settings, where a wrong flag can have serious consequences.
GPT
Short for Generative Pre-trained Transformer, the model architecture behind ChatGPT and many other AI writing tools. GPT models are trained on large text corpora to predict the next most likely word in a sequence.
Hallucination
When an AI model generates information that is fluent and confident but factually incorrect or entirely fabricated. Hallucinations are a key reason AI-generated content should be verified before publication.
Large Language Model (LLM)
An AI model trained on vast amounts of text to understand and generate human language. Examples include OpenAI GPT, Google Gemini, Anthropic Claude, and Meta Llama. LLMs power most modern AI writing tools.
Natural Language Processing (NLP)
The field of artificial intelligence focused on enabling computers to understand, interpret, and generate human language. AI detection relies on NLP techniques to analyze writing patterns.
Perplexity
A measure of how predictable a piece of text is to a language model. AI-generated text usually chooses the most statistically probable words, producing low perplexity, while human writing is less predictable and shows higher perplexity.
Prompt
The instruction or input given to an AI model to produce a response. The wording of a prompt strongly influences the style and content of the generated text.
Tokenization
The process of breaking text into smaller units called tokens (words or word fragments) that a language model can process. Detectors analyze text at the token level to evaluate predictability.
Training Data
The collection of text used to teach an AI model language patterns. The scope and quality of training data shape how a model writes and, in turn, the patterns detectors look for.
Transformer
A neural network architecture, introduced in 2017, that uses attention mechanisms to model relationships between words. Transformers are the foundation of modern large language models.
Watermarking
A technique that embeds a hidden, detectable signal into AI-generated text so it can later be identified as machine-produced. Watermarking is an emerging complement to statistical AI detection.
Zero-Shot Detection
Identifying AI-generated text without having been specifically trained on examples from that exact model, by relying on general statistical signals like perplexity and burstiness that hold across language models.
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