Retrieval-augmented generation (RAG) systems often experience answer drift in production, despite promising demos, and the root cause frequently lies not with the model itself, but with the document preparation process. Teams are discovering that the quality of retrieved text directly impacts answer accuracy, emphasizing the need to prioritize document cleaning, compression, and semantic retrieval over simply scaling up models. Ultimately, investing in a robust document layer—including data observability and purpose-aware encoding—can reduce token costs and improve compliance, particularly in regulated industries.
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