Part 2 — Why Does One System Need Three Chunking Strategies? And One Document Type Shouldn't Be Chunked At All
This article details the development of a sophisticated Chunking Service designed to improve retrieval quality in large language model applications. The service moved beyond a single fixed-size chunking strategy to implement three distinct approaches tailored to different document types. This was necessary because a one-size-fits-all method proved inefficient, particularly when dealing with semantically distinct documents like ESG reports and GRI clauses. The new system classifies documents based on filename, page count, and content features to apply the optimal chunking strategy, significantly reducing retrieval errors. AI
IMPACT Optimized chunking strategies can improve the accuracy and efficiency of information retrieval in LLM-powered applications.