What is RAG?
RAG (Retrieval-Augmented Generation) is a technique that connects a language model to your own information sources so it answers with verifiable data instead of only with its training.
In practice, when faced with a question the system first retrieves the relevant fragments from your sources —documents, databases, internal knowledge— and passes them to the LLM so it composes the answer based on them, ideally citing the origin. This reduces fabrications and keeps the answer anchored to current information that belongs to the organization.
Why it matters
For an organization, the value lies in the AI speaking with its own knowledge and not with generalities. RAG delivers answers that are traceable back to their source, easy to update as the data changes, and it is a common piece inside an AI agent that needs to consult reliable information before acting. It is an alternative or complement to fine-tuning.
How we approach it at Codara
We connect your information sources to the agents we build inside Codara's agentic orchestration layer, with permissions and traceability back to the origin, so the answers rest on your real, verifiable knowledge.
Preguntas frecuentes
What is RAG for in a company?
It lets the AI answer with the organization's own knowledge —documents, databases, policies— citing the source, instead of relying only on what the model learned during training.
RAG or fine-tuning?
RAG gives the model access to your data at answer time and updates as the source changes; fine-tuning retrains the model with that data. You choose based on information freshness, cost and control; often they are combined.