Parser, Chunking, and Embedding Interactions in Retrieval-Augmented Generation over Indian Government Regulatory Documents
Optimizing RAG workflows for complex regulatory documentation through systematic component evaluation.
Retrieval-augmented generation (RAG) pipelines are typically assembled from independently-chosen components -- a document parser, a chunking strategy, and an embedding model -- yet these choices are rarely evaluated jointly, and evaluations that do combine them are usually run on...
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This research exposes how the synergy between document parsing and retrieval strategy significantly dictates RAG performance in complex, domain-specific environments like Indian regulatory archives. By treating these components as an integrated system rather than independent parts, developers can drastically reduce hallucination rates in high-stakes legal document analysis.
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