Chunking Isn’t Dead. One Size Doesn’t Fit All

Chunking remains a critical yet under-optimized component of Retrieval-Augmented Generation (RAG) systems, directly determining the success of data retrieval. While naive sliding window methods are standard, they often fail because fixed sizes either lose context in small segments or obscure fine details in large ones. A multi-window chunking strategy addresses this by indexing the same corpus multiple times at different scales and using Reciprocal Rank Fusion (RRF) to combine the results. This approach allows the system to treat each chunk as a "voter" for its parent document, effectively matching the specific granularity of a user's question. Testing across benchmarks like MTEB and FinanceBench demonstrates significant performance uplifts compared to single-size strategies. Future improvements focus on refining fusion models to account for document length and optimizing the cost-to-performance trade-off between embedding dimensions and the number of indexing windows.