Chunking Benchmark — Find the Best Strategy for Your RAG Data

I will run a systematic chunking benchmark for your RAG system — comparing fixed-size, recursive, semantic, and structure-aware chunking strategies across your actual documents and queries, measuring Recall@k and MRR for each configuration, and delivering a data-backed recommendation for the optimal chunking strategy and parameters. Chunking strategy intuition is unreliable — what seems like the logical approach for a document type frequently underperforms a counterintuitive alternative on actual query distributions; only empirical benchmarking on your specific content and queries provides reliable guidance for chunking decisions.

The benchmark covers implementation of all four chunking strategies, retrieval quality evaluation across 30+ representative queries, chunk size sensitivity analysis for fixed-size approaches, overlap sensitivity analysis, combined strategy comparison (different strategies for different document types), and a recommendation report with supporting data.