RAG (Retrieval-Augmented Generation)
An AI pattern where an LLM retrieves relevant data from a knowledge base before generating a response. In automation: when a support ticket arrives, Claude searches your internal docs, then drafts a specific, accurate reply not a generic one.
Expert insight
RAG is why AI-powered support can answer "What's our refund policy for orders over £500?" accurately, not just generically.
In a real workflow
A support assistant retrieves the three most relevant help articles and answers using only that content.
Common mistakes
- Indexing outdated documents.
- Not citing sources, so answers cannot be checked.
Complexity level
Technical term used in production automation systems.
See it in action
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Go deeper
Guides that cover RAG
AI agents in 2026: what actually works and what is still hype.
95% of AI pilots fail. The 5% that succeed share three things in common. Here is what separates demo-ready from production-ready AI.
AI agents in production: what actually works vs what sounds good in demos.
The gap between a compelling AI agent demo and a reliable production deployment is wider than most vendors admit. Here is what we have learned building both.
How to build an AI agent with n8n and Claude: the complete technical guide
AI agents in production are not magic they are structured workflows with an LLM at the core, surrounded by memory, tools, error handling, and human oversight. Here is exactly how we build them.