A type of AI model trained on vast text data to understand and generate human-like language, powering chatbots, content tools, and search features.
An LLM is an AI model trained to generate and transform text. It can summarise, rewrite, classify, and reason across language patterns. The key limitation is that an LLM can sound confident while being wrong unless it’s grounded in reliable sources.
Search is increasingly “answer-like”. The safest strategy is to publish content that’s easy to cite: clear definitions, direct answers, and verifiable statements with supporting context.
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.
AI Hallucination
A phenomenon where a large language model generates false or illogical information but presents it as fact.
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.
Related GEO services, audits, and frameworks that operationalise Large Language Model (LLM) in commercial execution.
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