Why Retrieval Quality, Not the LLM, Often Limits RAG System Accuracy

A RAG system can produce a fluent, confident answer even when the document containing the decisive evidence was never retrieved. Retrieval-augmented generation is simple to describe but harder to build reliably, since locating text on a topic differs from locating the evidence needed to answer a specific question. The article argues that search relevance and answer relevance are distinct, so a semantically related passage may still omit the condition that determines the correct response. Chunking strategy is also a data modeling decision: splitting documents at fixed sizes can separate exceptions from the rules they modify, and losing headings or context can make retrieved values misleading. Because retrieval is frequently the real point of failure, upgrading the model or tweaking prompts may add cost without fixing the underlying problem.