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addresses one of the core limitations of LLMs: they can only know what they were trained on, and that knowledge goes out of date.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.
Related Terms
Hallucination
When an AI confidently produces false information it has invented.
Vector Database
A database that stores embeddings and allows searching by meaning rather than keywords.
Embedding
A numerical representation of a word or concept that captures its meaning and relationships.
Grounding
Connecting AI outputs to verified real-world information to reduce hallucination.
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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)