RAG (Retrieval-Augmented Generation)
AI & Emerging Tech
Giving an AI model the right documents to read before it answers.
In practice
Retrieval-augmented generation is the pattern of searching your own content — documents, records, a knowledge base — for what is relevant to a question, and handing that to the model alongside the prompt so its answer is grounded in your material rather than in what it happened to memorise. It is how most trustworthy AI products keep the model honest.
Questions
What is RAG (Retrieval-Augmented Generation)?
RAG is fetching relevant source material at question time and giving it to the model, so it answers from your content instead of from memory.
Why do you use RAG (Retrieval-Augmented Generation)?
Because a model on its own will confidently invent things. Grounding it in retrieved text is the cheapest reliable way to make answers accurate and citable.
How do you use/apply RAG (Retrieval-Augmented Generation)?
Index your content, search it when a question arrives, pass the best matches into the prompt, and show the person what was used so they can check.
When do you use RAG (Retrieval-Augmented Generation)?
Any product answering questions about specific material — your docs, your catalogue, your history — where being wrong has a cost.
Who uses RAG (Retrieval-Augmented Generation)?
Product teams building assistants and search, and designers deciding how sources and confidence are shown.
