Poor Retrieval, Not the LLM, Is Usually Behind RAG Hallucinations

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A software developer observed that a customer's RAG application consistently produced confident but incorrect answers despite months of development and multiple LLM swaps. The root cause was traced not to the language model itself, but to the retrieval layer supplying it with flawed context. Document chunking — commonly treated as a routine technical step — was identified as a key culprit, as mechanical token-based splits often sever related content and degrade answer quality. The author advocates for semantic chunking strategies that respect document structure and detect natural topic boundaries using cosine similarity between sentence windows. This approach, now integrated into tools like LangChain and LlamaIndex, helps ensure the model receives coherent, relevant context before any generation occurs.

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