AI glossary
What is RAG?
Retrieval-Augmented Generation (RAG) is an AI technique that retrieves relevant information from your own documents or database and feeds it to a large language model so it can answer from facts rather than memory.
A plain language model only knows what it was trained on — it can't see your product docs, help centre or internal data, and it will sometimes confidently make things up. RAG fixes this by adding a retrieval step: when a question comes in, the system searches your knowledge base for the most relevant passages and hands them to the model as context before it answers.
The result is an assistant that answers from your actual content, stays current as your data changes, and cites where the answer came from — dramatically reducing hallucinations. It's the backbone of most useful AI support bots, internal search tools and 'chat with your docs' features.
At Flowdira we build RAG features into client products — support assistants, documentation search and lead-qualification bots — using vector search over your content so the AI is genuinely grounded in your business.
Common questions
How is RAG different from just using ChatGPT?
Does RAG stop AI from hallucinating?
Want this built into your product?
We design and integrate fast websites and real AI features — from RAG assistants to lightning-fast front-ends.