RAG Pipelines Explained: Giving LLMs Access to Your Business Data

Retrieval-Augmented Generation (RAG) is becoming the dominant pattern for enterprise AI. This post demystifies how it works and when it's the right architecture for your use case.

Large language models know language — not your business. RAG bridges that gap by retrieving relevant documents at query time and feeding them to the model as context, grounding answers in your actual data.

How a RAG pipeline works

Documents are chunked, embedded, and stored in a vector database. When a user asks a question, the system retrieves the most relevant chunks, assembles a prompt, and the LLM generates an answer citing that context.

Quality depends on chunking strategy, embedding model choice, metadata filtering, and retrieval ranking — not just which LLM you pick.

When RAG is — and isn't — the answer

RAG excels at knowledge retrieval: policy Q&A, technical documentation search, and sales enablement. It's weaker for tasks requiring precise numerical computation or real-time transactional data — pair it with traditional APIs for those.

For South African businesses, hosting choices matter: where embeddings and source documents reside, who can query them, and how audit logs are retained under POPIA.