Techniques to anchor AI outputs in verified sources and facts, reducing hallucinations and improving reliability.
Grounding is the practice of constraining an AI model’s response to trusted sources - such as your documentation, a database, or specific URLs. Instead of “guessing” from general training data, the model is guided to use provided context, which reduces incorrect claims and improves consistency.
Grounding pairs naturally with good information architecture: the clearer your source content is, the easier it is for AI systems (and people) to reuse it accurately.
Browse related definitions in the same glossary category.
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.
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 Grounding in commercial execution.
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