RAG Explained - Introduction

General AI knows language. It does not know your policy PDF.

That gap is why enterprise teams stop at the demo. The model answers fluently about refunds, retention, or last quarter’s outage — and quietly invents the paragraph it never saw. Retrieval-augmented generation (Lewis et al., 2020) is the named fix: index your corpus, retrieve a few passages at ask-time, then generate with those passages in context. Same language model. Different trust boundary.

From Models to Agents maps eras and the enterprise playbook. How AI Agents Actually Work Part 6 places RAG on the agent stack — retrieve-then-ground inside a loop. This series is the enterprise path: why your data matters, how the architecture runs in operations, what products like NotebookLM spell the pattern, and how to choose RAG versus an LLM alone, traditional automation, or a full agent — including when an agent is the wrong tool.

What you will get

Five parts. One decision frame. No vendor bake-off.

Your corpus  →  index  →  retrieve k  →  generate
                              │
                    permissions, freshness, eval

Read the pipe left-to-right once. Everything else in the series hangs off that line.

The series

1. Why your data — models without your corpus; grounding vs guessing
2. Architecture — index, retrieve, generate; operational examples
3. Enterprise ops — permissions, chunking, stale indexes, wrong chunks
4. Product instances — NotebookLM and verified peers as pattern spellings
5. RAG vs agents vs automation — pick the right tool; when not to use an agent

Sources

• Lewis et al. — Retrieval-Augmented Generation (arXiv:2005.11401)

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Part 1: Why your data