AI detection glossary
Plain-language definitions for the terms you'll meet in AI detection — for developers, educators, editorial teams, and procurement. 16 terms, more added as the field moves.
Detection
AI detection
The task of identifying text that was written by a large language model rather than a human.
Confidence score
A 0-to-1 value reflecting how certain the detector is that a piece of text was AI-generated.
Sentence-level scoring
Returning an AI-probability score for each individual sentence in a document, not just one number for the whole thing.
Watermarking
Embedding a statistical signal in AI-generated text that detectors can later check for.
Statistics
Burstiness
A measure of variation in sentence length, structure, and complexity across a piece of text.
False negative
When a detector misses AI-generated text and classifies it as human.
False positive
When a detector flags human-written text as AI-generated.
Perplexity
How surprised a language model is by a given piece of text — lower means the text looks more model-generated.
Stylometry
The statistical analysis of writing style — historically used for author identification.
Models
Hallucination
When an LLM generates output that is fluent but factually wrong or fabricated.
Humanizer
A tool that rewrites AI-generated text to evade detection — and the arms race that comes with it.
LLM (large language model)
A neural network trained on huge text corpora to predict the next token — the engines behind ChatGPT, Claude, Gemini, etc.
Token
The smallest unit of text a language model processes — usually a word or a piece of a word.
Workflow
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