Visualizing and Comparing RAG Chunking Strategies
victorialslocum · x · 2026-07-16
This content emphasizes that when RAG fails, the root cause isn't necessarily the retrieval model or vector database, but often the chunking strategy. Poor chunking might mix multiple topics or strip away the context needed by the LLM, making it look like a broken retriever.
The author presents an interactive demo where users can upload their own data or use sample data to compare the effects of 7 chunking strategies: fixed-size, recursive, document-structure-based, semantic, LLM-driven, agentic hybrid, and hierarchical. Its value lies in visualizing abstract trade-offs rather than just theorizing.
The core conclusion: no single chunking strategy fits all scenarios. The best choice depends on the document type, query complexity, embedding model, and desired retrieval granularity. Only by seeing the actual chunked results can you determine which trade-off suits your data.
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