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Over the past two years RAG evolved from a 'linear pipeline' to 'loop-based reasoning'. It maps cleanly to five stages: Naive, Advanced, Modular, Graph, Agentic. The real inflection point is control moving from pipeline to agent — a System 1 → System 2 shift. Looking back at engineer-news's own RAG stack, it's stuck at the Naive edge — so this post also lays out what to fix next.
The previous post zoomed out for a five-stage panorama of RAG. This one zooms in on the five infra lessons any real RAG has to face: document ingestion, contextualized chunking, three-lane hybrid search, tensor reranker, and GraphRAG's semantic gap. Each lesson is checked against engineer-news's current stack, ending with a priority list for a personal site.
When an agent loads tools on demand, it's essentially applying RAG's 'retrieve-then-inject' pattern to tool schemas — the only difference is the retriever is the LLM's own reasoning instead of vector similarity.
PageIndex swaps the vector DB for a hierarchical tree index plus an LLM Agent that reasons over it, and it shines on long structured documents (98.7% on FinanceBench). This site's Hybrid RAG instead runs vector search with a keyword fallback on the Cloudflare edge — a completely different set of tradeoffs.
The LLM Wiki isn't a query tool—it's an architecture that lets knowledge compound over time. The LLM actively builds and maintains a markdown knowledge base, instead of re-fetching from raw documents on every single query.