The question that broke my RAG pipeline I had a solid RAG setup. Embeddings, vector store, top-k retrieval, the whole thing. It handled factual lookups just fine: "What's the API rate limit?" "Which config file controls logging?" Quick, accurate, done. Then a teammate asked: "What technical challenges do Project A and Project B have in common?" The system returned chunks about Project A. Chunks about Project B. Individually relevant. But it never connected the dots between them. It couldn't, because vector search finds similar documents -- not related ones. Those are fundamentally different operations. I spent a solid week rewriting prompts and adjusting chunk overlap before admitting the architecture itself was the bottleneck. A week I'd like back. This is the structural ceiling of conventional RAG. What vector search actually can't do Standard RAG works by converting text into embeddings, then finding the chunks closest to the query in vector space. If your question maps neatly to a single chunk, it works.…