A phenomenon where a large language model generates false or illogical information but presents it as fact.
An AI hallucination is when a model produces confident-sounding information that’s incorrect, unsupported, or fabricated. It can happen because models optimise for plausible language, not truth. In marketing workflows, hallucinations typically show up as invented statistics, fake citations, incorrect product details, or overly specific claims.
Hallucinations aren’t a reason to avoid AI - they’re a reason to design a workflow that treats AI output as a draft until it’s grounded and verified.
Browse related definitions in the same glossary category.
AI Grounding
Techniques to anchor AI outputs in verified sources and facts, reducing hallucinations and improving reliability.
Citation Confidence
A measure of how accurately an AI model attributes information to its original source.
Content Provenance (C2PA)
Technical standards for verifying the origin and authenticity of digital content, increasingly important for AI-generated media.
Embeddings
Numerical representations of text, images, or other data that capture semantic meaning, enabling similarity search and machine learning.
Fine-Tuning
Adapting a pre-trained AI model to a specific task or domain by training it on additional specialised data.
Large Language Model (LLM)
A type of AI model trained on vast text data to understand and generate human-like language, powering chatbots, content tools, and search features.
Related GEO services, audits, and frameworks that operationalise AI Hallucination in commercial execution.
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