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Retrieval, Memory & Knowledge

RAG (Retrieval-Augmented Generation)

Combining an LLM with a search system so it can look up current or specific information before responding.

Definition

Retrieval-augmented generation is a technique that addresses one of the core limitations of LLMs: they can only know what they were trained on, and that knowledge goes out of date. RAG connects the model to an external knowledge base (your company's documents, a product database, a legal library) and retrieves relevant information at query time, feeding it into the prompt before generating a response. This dramatically reduces hallucination for fact-heavy tasks and allows the model to access current, proprietary information.

Why this matters for your business

RAG is the most commercially important technique for enterprise AI deployment. It allows businesses to build AI that answers questions based on their own documentation, policies, and knowledge, without exposing that data during model training.

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Disclaimer

This definition is provided for educational and informational purposes only. It represents a general explanation of a technical concept and does not constitute professional, technical, or investment advice. Artificial intelligence is a rapidly evolving field; terminology, techniques, and capabilities change frequently. Coaley Peak Ltd makes no warranty as to the accuracy, completeness, or currency of the information provided. Nothing on this page should be relied upon as the sole basis for commercial, technical, legal, or investment decisions without independent professional advice.

Document reference: ISO_webpage_knowledge-base_glossary_v1

Last modified: 29 March 2026

Knowledge Base·Retrieval, Memory & Knowledge·RAG (Retrieval-Augmented Generation)