The original headline is: "Why Most RAG Systems Fail in Production: The Hidden Architecture Problems Behind AI Search"

Short summary

This article explains why naive RAG implementations that chain an LLM to a vector DB fail in production. Real production RAG requires architecture covering ingestion, chunking, metadata extraction, hybrid search, reranking, and evaluation. The LLM is often the smallest part; retrieval and data pipeline design determine whether the system is trustworthy.

• •Naive RAG demos work but production systems fail due to broken architecture, not broken software
• •Production RAG requires ingestion, chunking, metadata, hybrid search, reranking, and evaluation pipelines
• •The LLM is the smallest part of a production RAG system; retrieval design matters most

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