Evaluating Chunking Strategies For Retrieval-Augmented Generation in Oil and Gas Enterprise Documents

• URL: http://arxiv.org/abs/2603.24556v1
• Date: Wed, 25 Mar 2026 17:35:24 GMT
• Title: Evaluating Chunking Strategies For Retrieval-Augmented Generation in Oil and Gas Enterprise Documents
• Abstract summary: Retrieval-Augmented Generation (RAG) has emerged as a framework to address the constraints of Large Language Models (LLMs)<n>This paper presents an empirical study quantifying performance differences across four chunking strategies.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Retrieval-Augmented Generation (RAG) has emerged as a framework to address the constraints of Large Language Models (LLMs). Yet, its effectiveness fundamentally hinges on document chunking - an often-overlooked determinant of its quality. This paper presents an empirical study quantifying performance differences across four chunking strategies: fixed-size sliding window, recursive, breakpoint-based semantic, and structure-aware. We evaluated these methods using a proprietary corpus of oil and gas enterprise documents, including text-heavy manuals, table-heavy specifications, and piping and instrumentation diagrams (P and IDs). Our findings show that structure-aware chunking yields higher overall retrieval effectiveness, particularly in top-K metrics, and incurs significantly lower computational costs than semantic or baseline strategies. Crucially, all four methods demonstrated limited effectiveness on P and IDs, underscoring a core limitation of purely text-based RAG within visually and spatially encoded documents. We conclude that while explicit structure preservation is essential for specialised domains, future work must integrate multimodal models to overcome current limitations.

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