RAG (Retrieval-Augmented Generation)
Grounding an AI's answers in retrieved, relevant source data.
Retrieval-augmented generation combines an LLM with a retrieval step that fetches relevant documents or data, so the model answers using accurate, up-to-date, source-grounded information rather than memory alone.
RAG reduces hallucination and lets AI products cite sources — a pattern that also supports the trust and transparency UI users increasingly expect.
Key characteristics
- Combines a language model with a retrieval step over your own data.
- Relevant documents are fetched and added to the prompt as grounding context.
- Reduces hallucination and lets models answer about private or current information.
- Avoids retraining the model when knowledge changes — you update the data source.
Example
A support bot uses RAG to pull the three most relevant help articles for a question and answer from them, citing sources, instead of guessing from general training.
Frequently asked questions
Why use RAG instead of fine-tuning?
RAG keeps knowledge in an external, easily updated source and grounds answers in it, reducing hallucination. Fine-tuning bakes behaviour into the model and is better for style or format than for fresh, factual knowledge.
Does RAG eliminate hallucinations?
It greatly reduces them by grounding answers in retrieved facts, but doesn't fully eliminate them — retrieval can miss or the model can still misread context. Good source citation helps users verify.
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