Part 4 of 5 — RAG Explained

These products are versions of the same pipeline.

Part 2 drew index → retrieve → generate. Running that pipeline inside a company takes engineers, ACLs (Access Control Lists), and eval. Shipping it as a product wraps the same pattern in uploads, citations, and a UI. The names change. The loop does not.

This part maps a small set of verified products onto the pattern — not a ranking of “best RAG tools.”

Google NotebookLM

NotebookLM (external link) is Google’s document-grounded notebook: you add sources (PDFs, docs, pasted text), and the product retrieves from those sources when you ask questions or generate summaries. Official positioning emphasises sources you provide and citations back to those sources — the consumer version of retrieve-then-generate over an approved mini-corpus, not open-web guessing.

For a reader coming from Part 1, NotebookLM is the “your data, not the whole internet” idea in product form. It is not an agent with arbitrary tools unless Google’s product adds actions beyond generation from sources. Treat it as grounded Q&A and synthesis, not a replacement for your internal ACL-aware index.

Enterprise search + RAG (Azure AI Search pattern)

Large shops often already have enterprise search. Microsoft documents retrieval-augmented generation with Azure AI Search (external link) as: index your content in the search service, retrieve relevant passages at query time, pass them to a model for generation. It is the same pipeline as Lewis et al., run by different teams (the search team and the app team).

That pattern fits when your corpus is already in SharePoint, blob storage, or a custom indexer — and you need permissions, skillsets, and hybrid keyword + vector retrieval in one place. It is infrastructure RAG, not a notebook UI.

What these are not

  • Not a comparison. No star counts, no “NotebookLM beats X.”
  • Not agents. Neither example is “dynamic multi-step workflow with arbitrary tools” by default. Part 5 is the decision frame.
  • Not your compliance program. Uploading HR PDFs to a consumer notebook without policy review is still a policy mistake.

Map products to the diagram

NotebookLM:     your uploads ──▶ index in product ──▶ Q&A with citations
Azure RAG:      corp content ──▶ AI Search index ──▶ retrieve ──▶ Azure OpenAI (typical)
Your pipeline:  [Part 2 diagram] — you own every box

Pick the implementation that matches who runs the index and where ACLs (Access Control Lists) must live. Skip the product if it cannot meet your data boundary.

Takeaway: NotebookLM and enterprise search RAG are named instances of retrieve-then-generate — useful to recognise the pattern, not to declare a winner.

Sources