overlapping chunks

**Overlapping chunks** is the **chunking design that repeats boundary-adjacent tokens across neighboring chunks to preserve continuity** - overlap reduces information loss when answers straddle chunk borders. **What Is Overlapping chunks?** - **Definition**: Chunking strategy where consecutive chunks share a configurable token window. - **Mechanism**: Chunk N includes tokens later repeated at start of chunk N+1. - **Purpose**: Protect context across boundaries in fixed or sentence-packed chunking. - **Design Variables**: Overlap width relative to chunk size and document type. **Why Overlapping chunks Matters** - **Boundary Robustness**: Prevents answer fragmentation caused by hard splits. - **Recall Gains**: Increases chance at least one chunk contains full relevant span. - **RAG Reliability**: Improves retrieval coverage for multi-sentence facts. - **Tradeoff Cost**: Raises index size and may increase duplicate retrieval hits. - **Generation Stability**: Better continuity reduces incoherent evidence stitching. **How It Is Used in Practice** - **Overlap Tuning**: Start with 10 to 20 percent overlap and adjust by retrieval metrics. - **Dedup Handling**: Merge near-duplicate hits during reranking and context assembly. - **Policy Segmentation**: Use larger overlap for narrative text, smaller for structured docs. Overlapping chunks is **a practical reliability enhancement in document ingestion** - controlled overlap often improves recall and grounding fidelity with manageable indexing overhead.