What is RAG (retrieval-augmented generation)?
Retrieval-augmented generation (RAG) is a technique where an AI looks up relevant information before answering, then uses it to write the response. Instead of relying only on training data, the model is fed current, specific documents, so answers are grounded in your data and less likely to be made up.
Retrieval-augmented generation closes the biggest gap in using AI for real work: a model does not know your business. RAG fixes that at answer time by retrieving the relevant information first, then generating a response grounded in it.
How RAG works
- Your documents are indexed so they can be searched by meaning.
- When a question comes in, the system retrieves the most relevant passages.
- Those passages are given to the LLM along with the question.
- The model answers using that specific, current information, ideally with sources.
RAG vs. giving an agent tools
RAG is one way to ground answers in your data. AI agents can go further, using tool calling to query live systems directly. Many setups combine both.
RAG in Oido
Oido agents can ground their answers in your knowledge and live systems, then deliver the result to the channels your team uses, running on whichever model provider you choose.
Frequently asked
It lets an AI answer using your own documents, policies, and data, accurately and with sources, instead of generic training knowledge. That makes it practical for support, internal knowledge, and anything needing current, company-specific facts.
Fine-tuning changes the model by training it further; it is costly and static. RAG leaves the model as-is and feeds it relevant information at answer time, so it stays current as your data changes. Most teams reach for RAG first.
It reduces it substantially by grounding answers in retrieved sources, and lets you show citations. It does not eliminate errors entirely, so review still matters for high-stakes answers.