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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.
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.
A pnpm monorepo integrating four tool SPAs: a to-do list, a habit tracker, an ebook reader, and a resignation stamp collector. apps/root is the sole build entry point; the four modules are library-only workspace packages bundled together by root's Vite, sharing @a920604a/auth and @a920604a/ui, deployed across Firebase + Cloudflare dual cloud.
A pure-TypeScript tag scoring engine computes real-time risk across 140 foods for four conditions — gout, high cholesterol, diabetes, and hypertension. The entire backend runs on Cloudflare's free tier, for a monthly cost of $0.
Astro handles static rendering and content management, Cloudflare Pages/Workers handle deployment and dynamic APIs, D1 provides lightweight data storage, Vectorize + Workers AI power RAG semantic search, and R2 stores OG images and TTS audio. The entire pipeline — from YouTube crawl to user search — runs inside the Cloudflare ecosystem.