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RAG for Business: A Practical Guide to Turning Company Docs into an AI Assistant

Retrieval-augmented generation (RAG) is one of the fastest ways to get real value from AI using your internal knowledge. This guide explains what to build, how to measure quality, and how to deploy it safely.

What is RAG?

RAG combines a language model with a search layer over your company documents. Instead of guessing, the model answers with relevant context retrieved from your knowledge base.

When RAG is the right choice

• You have lots of PDFs, SOPs, tickets, or internal docs.
• Answers must reflect current policies and sources.
• You need access control and auditability.

A practical architecture (high level)

1. Ingest: Collect documents and normalize formats.
2. Chunk: Split into useful, source-linked passages.
3. Embed: Create vector representations for fast retrieval.
4. Retrieve: Find the best chunks per question.
5. Generate: Answer using retrieved context, with citations.

Security and compliance basics

• Restrict access by user/team and document sensitivity.
• Log prompts and retrieval sources for audits.
• Use redaction for PII where required.

How to measure quality

Quality is measurable. Start with a realistic test set of questions, then track: retrieval precision, factuality, and user satisfaction.