There's a moment in every RAG project where you realize you're not just building a search engine — you're building a knowledge system. And that distinction matters more than you'd think.
The Indexing Problem
When I first started building retrieval-augmented generation systems, I thought the hard part would be the generation. Getting an LLM to produce coherent answers seemed like the real challenge. I was wrong.
The hard part is retrieval. More specifically, the hard part is deciding how to chunk, embed, and index information so that the right context surfaces at the right time.
What Books Already Knew
Here's what struck me: the problems I was solving with vector databases and embedding models are the same problems that book publishers solved centuries ago with tables of contents, indexes, and chapter structures.
A well-organized book doesn't just contain information — it makes that information findable. The same principle applies to RAG systems. Your chunking strategy is your table of contents. Your embedding model is your indexer. Your retrieval pipeline is the reader scanning for relevant passages.
The Lesson
Building RAG systems taught me that knowledge isn't just about having information. It's about organizing it for retrieval. Whether you're writing a book or building an AI system, the architecture of access matters as much as the content itself.
This changed how I think about documentation, note-taking, and even how I structure my own learning. Every piece of knowledge I encounter, I now ask: how will I find this again when I need it?