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 (external link)) is the named fix: index your corpus, retrieve a few passages at ask-time, then generate with those passages in context. The language model is the same. What changes is the information it is allowed to rely on.
From Models to Agents covers the eras of AI and the enterprise approach. 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, how products like NotebookLM put the pattern into practice, 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 way to decide, and no comparison of vendors.
Your corpus → index → retrieve k → generate
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permissions, freshness, evalRead the pipeline once from left to right. Everything else in the series hangs off that line.
The series
- Why your data — models without your corpus; grounding vs guessing
- Architecture — index, retrieve, generate; operational examples
- Enterprise ops — permissions, chunking, stale indexes, wrong chunks
- Product instances — NotebookLM and similar verified products as examples of the pattern
- RAG vs agents vs automation — pick the right tool; when not to use an agent