RAG (Retrieval Augmented Generation)
Quick Answer
Retrieval Augmented Generation (RAG) is an architecture that combines a language model with an external knowledge source. Before generating an answer, the system retrieves relevant documents and feeds them to the model as context, dramatically reducing hallucinations and keeping answers current.
In Depth
What RAG (Retrieval Augmented Generation) really means
A typical RAG pipeline indexes your documents into a vector database, retrieves the most relevant chunks for each user query, and injects them into the prompt so the LLM can ground its answer in your organisation's actual content.
RAG is often the cheapest and safest way to make an LLM useful for enterprise use cases. It avoids expensive fine-tuning, keeps proprietary data out of the base model, and allows you to update knowledge simply by updating your document store.
Why It Matters
Business relevance for UK organisations
RAG is the architecture of choice for organisations building internal chatbots over policy documents, HR handbooks, product manuals or knowledge bases. It is auditable, updateable and respects data governance boundaries.
Real-world example
How this shows up in practice
A Nottingham manufacturer built a RAG system over 14,000 pages of equipment manuals, allowing engineers to ask troubleshooting questions and receive answers with exact page citations.
Related Terms
Continue exploring
Large Language Model (LLM)
A Large Language Model (LLM) is a type of neural network trained on vast quantities of text to understand and generate human language. LLMs power chatbots, copilots, content generators and many modern AI features across consumer and business software.
TechnicalEmbedding
An embedding is a numerical vector representation of text, images or other data that captures semantic meaning. Items with similar meaning produce similar vectors, which makes embeddings the backbone of semantic search, recommendations and RAG systems.
TechnicalVector Database
A vector database is a specialist datastore designed to index and query high-dimensional vectors, typically embeddings, at scale. It enables fast similarity search — retrieving the most semantically related items for a given query vector — across millions or billions of items.
TechnicalHallucination
A hallucination is when an AI model produces output that sounds plausible but is factually incorrect, fabricated or inconsistent with its sources. Hallucinations are a fundamental property of current generative models and the single biggest risk in enterprise deployments.
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