Chunking Strategy
A set of technical approaches used in RAG pipelines and local indexing to split documents or code into retrievable units. Common strategies include fixed-length chunking, semantic-boundary chunking, and sliding-window chunking. The chosen granularity directly affects the trade-off between retrieval precision and contextual completeness.
Timeline (last 90 days)
When recall@5 on adversarial questions is low, the correct action is to go back and check the chunking strategy; only after recall@5 meets the bar is it meaningful to invest in optimizing the embedding model or reranker.
分块大小没有万能值，256-512 token是常见起点，但技术文档可能需要按标题层级分块。
Common chunking methods include splitting by fixed character count, splitting by sentence/paragraph boundaries, and sliding splits with overlapping windows
向量检索的效果高度依赖于分块策略，常用方法包括固定长度分块、基于语义的分块以及递归分块
All Facts (4)
When recall@5 on adversarial questions is low, the correct action is to go back and check the chunking strategy; only after recall@5 meets the bar is it meaningful to invest in optimizing the embedding model or reranker.
50%Unverified分块大小没有万能值，256-512 token是常见起点，但技术文档可能需要按标题层级分块。
50%UnverifiedCommon chunking methods include splitting by fixed character count, splitting by sentence/paragraph boundaries, and sliding splits with overlapping windows
50%Unverified向量检索的效果高度依赖于分块策略，常用方法包括固定长度分块、基于语义的分块以及递归分块
50%