The Manual RAG Pipeline: 'Embed It and RAG It' — and Where It Works
~11 min read
The book's framing of the naive default approach to context retrieval: embed the data, store it in a vector DB, do RAG. It works beautifully for static sources — the next subtopic covers exactly where it doesn't.
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Key points
- •The book's framing of the default developer approach: embed the data, store it in a vector DB, and do RAG — the standard manual pipeline
- •This recipe matches the standard RAG workflow: chunk documents, embed each chunk, store vectors, retrieve top-k similar chunks at query time
- •The book is direct that this works beautifully for STATIC sources — a fixed document collection with mature, well-understood tooling
- •The book's pivot is equally direct: no real-world workflow looks like this — most real data lives scattered across many different, constantly-changing systems
- •The manual pipeline isn't wrong, it's solving a narrower problem (few static sources) than what most real enterprise data actually presents