RAG has evolved far beyond simple 'search + generate' into a technology ecosystem spanning ten generations. This article is a systematic navigation guide: from Naive RAG to Multi-Agent RAG across ten generations, covering retrieval strategies, chunking, embedding, reranking, evaluation frameworks, observability, and cost optimization. Each topic has a dedicated deep-dive article.
When a RAG system breaks, 90% of the time it's one of these 10 failure modes. Identify which one first, then apply the matching fix — far more effective than optimizing blindly.
Vector search handles semantics; BM25 handles keywords. Combining them with RRF is what lets you handle both fuzzy queries and exact terms at the same time.
A single RAG Agent handling all queries hits knowledge boundaries and performance bottlenecks. Multi-Agent RAG dispatches retrieval tasks to multiple specialized Agents, each with its own knowledge base and retrieval strategy, coordinated by a central Orchestrator that merges results.
Using Weaviate Query Agent + ColQwen multi-vector model, a single prompt built a production-grade legal contract search system in 36 hours -- this post breaks down its architecture logic, technology choices, and what you actually need to watch out for.
PageIndex skips chunking, embedding, and vector storage entirely. Instead it relies on LLM reasoning over a tree-structured table of contents the LLM itself wrote, achieving 98.7% on FinanceBench (GPT-4o reading directly scores only 31%). It solves a different problem than vector RAG — finding the right section in a well-structured long document.