Adapting a pre-trained AI model to a specific task or domain by training it on additional specialised data.
Fine-tuning is a way to adapt a base AI model using your own examples so it behaves more consistently for a specific task (tone, classifications, structured outputs, customer support flows). It’s most valuable when prompt-only approaches are too inconsistent or too expensive to run at scale.
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.
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 Fine-Tuning in commercial execution.
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