# Retrieval-Augmented Generation (RAG) Explained 2026 > Source: > Published: 2026-08-15 18:00:00+00:00 A general-purpose AI model knows a lot about the world and nothing about your business. It has never seen your product manuals, your internal policies, or last quarter's reports. Retrieval-augmented generation (RAG) is the technique that closes that gap: it lets a model answer questions using *your* documents, accurately and with sources, without retraining the model. This guide explains what RAG is, how it works, and when to use it. **TL;DR** Language models have two limits for business use: they only know what was in their training data (so nothing private and nothing recent), and they can confidently make things up. Feeding all your documents into every prompt is not feasible; there is too much, and it would be slow and expensive. RAG solves both by fetching only the *relevant* pieces for each question and grounding the answer in them. There are two phases. **Indexing (done once, and updated as content changes):** **Retrieval and generation (at query time):** Because retrieval is based on meaning rather than exact keywords, RAG finds relevant content even when the wording differs. Fine-tuning adjusts the model's weights on your data. It has its place, but for knowledge-based answering RAG is usually the better choice: Fine-tuning is better for teaching a consistent *style* or *format*, or narrow specialised behaviour, not for keeping a body of knowledge current. RAG is simple in concept and easy to do badly. Quality depends on: A production RAG system needs the right chunking, retrieval, prompting, and an indexing pipeline that stays current. The [AI integration services](https://mecanik.dev/en/ai-integration-services/) build RAG-powered knowledge bases and other intelligent features into your existing applications with engineered prompts and cost controls, and the [OpenAI API integration service](https://mecanik.dev/en/openai-api-integration/) covers RAG-backed assistants specifically. If you are starting with a conversational interface, see [building an AI chatbot with the OpenAI API](https://mecanik.dev/en/posts/building-an-ai-chatbot-with-the-openai-api/). For a broader view of adopting AI, the [AI integration guide for UK SMEs](https://mecanik.dev/en/posts/ai-integration-for-uk-smes-a-practical-guide-for-2026/) is a good starting point. **Related reading:** [Build an OpenAI API Chatbot: A 2026 Guide](https://mecanik.dev/en/posts/building-an-ai-chatbot-with-the-openai-api/), [Claude API vs OpenAI API: A Developer's Comparison 2026](https://mecanik.dev/en/posts/claude-api-vs-openai-api-for-developers/), [AI Integration for UK SMEs - A Practical Guide for 2026](https://mecanik.dev/en/posts/ai-integration-for-uk-smes-a-practical-guide-for-2026/) and [AI Agency vs In-House: UK AI Adoption in 2026](https://mecanik.dev/en/posts/ai-agency-vs-in-house-uk-ai-adoption-in-2026/). **What is retrieval-augmented generation (RAG)?** RAG is a technique that lets an AI model answer using your own documents. It retrieves the most relevant snippets from your content and includes them in the prompt, so the model responds based on your knowledge rather than only its training data. **How is RAG different from fine-tuning?** RAG retrieves relevant content at query time and grounds the answer in it, so updates are instant and sources can be cited. Fine-tuning changes the model's weights and needs retraining to reflect new information. RAG is better for current knowledge; fine-tuning for consistent style or narrow behaviour. **Does RAG stop AI hallucinations?** It significantly reduces them by grounding answers in retrieved facts, and it lets you cite sources so answers are auditable. It does not eliminate hallucination entirely, so good prompting (including instructing the model to say when it does not know) still matters. **What is a vector database and why does RAG need one?** A vector database stores embeddings, the numerical representations of your document chunks, and finds the ones closest in meaning to a question. RAG uses it to retrieve relevant context quickly based on meaning rather than exact keyword matches. **What do I need to build a RAG system?** Your source documents, a chunking and embedding pipeline, a vector database, and an application that retrieves relevant chunks and prompts the model with them. The harder parts are chunking strategy, retrieval quality, and keeping the index current.