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What is RAG (Retrieval-Augmented Generation)?

Quick Answer

RAG (Retrieval-Augmented Generation) is a technique that enhances large language models by retrieving relevant information from your own documents and data before generating a response. Instead of relying solely on the model's training data, RAG grounds answers in your specific content, dramatically reducing hallucinations and ensuring responses are accurate, current, and relevant to your organisation.

Summary

Key takeaways

  • Combines the power of LLMs with your organisation's specific knowledge
  • Significantly reduces AI hallucinations by grounding responses in real documents
  • Does not require expensive model training or fine-tuning
  • Can be updated instantly by adding or modifying source documents

How RAG Works

RAG operates in two stages. First, when a user asks a question, the system searches a pre-built index of your documents to find the most relevant passages. This retrieval step uses vector embeddings to match the meaning of the query against the content of your documents, not just keywords. Second, the retrieved passages are passed to a large language model alongside the original question. The model then generates an answer that synthesises information from the retrieved content, citing sources where possible. This approach means the AI's responses are grounded in your actual data rather than the model's general training knowledge. The document index can be updated at any time without retraining the model, keeping your AI system current as policies, procedures, and information change.

When to Use RAG

RAG is ideal when you need an AI system to answer questions about your organisation's specific content: internal policies, product documentation, legal documents, technical manuals, or customer-facing knowledge bases. It excels when accuracy is critical and you need the AI to cite its sources. RAG is also the right choice when your information changes frequently, as you simply update the document index rather than retraining a model. It works well with any volume of content, from a handful of documents to millions of pages. RAG is generally preferred over fine-tuning for most business applications because it is faster to set up, easier to maintain, and provides better source attribution.

Key Implementation Considerations

Effective RAG implementation requires attention to several factors. Document chunking strategy matters significantly: splitting documents into appropriately sized passages affects retrieval quality. Embedding model selection influences how well the system understands semantic meaning. The retrieval algorithm and number of passages returned need tuning for your specific use case. Prompt engineering ensures the language model uses the retrieved content effectively. Testing and evaluation should cover accuracy, relevance, and completeness of responses. Most organisations start with a basic RAG implementation and iteratively improve retrieval quality, adding techniques like hybrid search, re-ranking, and metadata filtering as needed.

Related

Related questions

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FAQ

Frequently asked questions

RAG significantly improves accuracy by grounding responses in your actual documents. Studies show RAG reduces hallucination rates from 15-25% with standard LLMs to 2-5% when properly implemented. Accuracy depends on document quality and retrieval configuration.

A basic RAG system can be built for £10,000 to £30,000. Enterprise implementations with advanced features, security, and integration typically cost £30,000 to £100,000. Ongoing costs include API usage, hosting, and maintenance.

Yes. RAG can be deployed on-premise or in a private cloud to keep sensitive documents within your infrastructure. Access controls can restrict which users can query which document collections, maintaining information security boundaries.

A basic RAG system can be set up in 2 to 4 weeks. Production-quality implementations with proper chunking, retrieval tuning, and evaluation typically take 4 to 8 weeks. Enterprise deployments with security, access controls, and system integration may take 8 to 16 weeks.

RAG works best with unstructured text but can be adapted for structured data. Spreadsheet data can be converted into natural language descriptions for embedding, or hybrid approaches can combine RAG with traditional database queries for structured information.

Related Content

RAG Implementation Guide

Step-by-step guide to building a RAG system

Vector Databases Explained

Understanding the storage layer for RAG

AI Knowledge Base Service

Build a RAG-powered knowledge base for your organisation

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