Most organizations already have the answers they need. The problem is that those answers are scattered across documents, messages, project tools, and shared drives.
What RAG changes
Retrieval-augmented generation searches an approved knowledge base before producing an answer, allowing the model to respond using current company information and cite its sources.
A useful system needs more than embeddings
Document quality, permissions, chunking, metadata, ranking, and evaluation matter as much as model choice.
High-value applications
Internal policy assistants shorten search time. Customer support copilots surface product details. R&D assistants preserve decisions and connect findings across project phases.
How to begin
Choose one audience and a bounded collection of high-value documents. Build a question set from real users and measure answer accuracy and source quality.