Retrieval-Augmented Generation (RAG)
A technique that enhances AI responses by retrieving relevant information from a knowledge base before generating a response — grounding AI output in accurate, up-to-date information.
Retrieval-Augmented Generation (RAG) solves one of the core limitations of large language models: their knowledge is frozen at training time and they cannot access information added after that cutoff. RAG gives AI agents access to current, domain-specific information by retrieving relevant content from a knowledge base before generating a response.
In a RAG system, when a user or another system queries the AI agent, the agent first searches a vector database (a database optimised for semantic similarity search) to find the most relevant documents, FAQs, policies, or records. This retrieved context is then passed to the LLM alongside the original query, and the LLM generates a response grounded in the actual retrieved information — not just its training data.
For business AI agents, RAG is essential for tasks that require knowledge of the organisation's specific policies, products, pricing, or processes. A customer support AI agent using RAG can answer questions about your specific return policy, product specifications, and current pricing — not just generic information about the category.
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Frequently Asked Questions
Does every AI agent need RAG?
Not necessarily. AI agents operating on structured data from integrated systems (a CRM, an ERP, a database) often don't need RAG — they retrieve data through API calls, not semantic search. RAG is most valuable for AI agents that need to answer open-ended questions from a large body of text (product documentation, policy documents, FAQ libraries, past support tickets). If your agent needs to 'know' a lot of unstructured information, RAG is the right approach.
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