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RAG Classifications, Architectures: A Field Guide for Production-Grade Systems

TL;DR AI

Key summary

2 min read
  1. Basic RAG often breaks in real production due to poor chunking, semantic mismatch, multi-hop queries, irrelevant retrieval, and unsupported generation.

  2. The article compares naive, advanced, and modular RAG, and highlights upgrades such as query rewriting, HyDE, re-ranking, and contextual filtering.

  3. It also surveys eight production patterns: Standard, Hybrid, GraphRAG, CRAG, Self-RAG, Adaptive RAG, Agentic RAG, and Multi-Modal RAG.

  4. The main takeaway is to match RAG design to the failure mode instead of assuming one pipeline fits every query.

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