sentence-based chunking

**Sentence-based chunking** is the **chunking method that groups complete sentences into retrieval units to preserve grammatical and semantic coherence** - it typically improves passage readability and answer extraction fidelity. **What Is Sentence-based chunking?** - **Definition**: Segmentation approach using sentence boundaries, then packing sentences up to a target size. - **Boundary Source**: Uses NLP sentence splitters with language-specific punctuation rules. - **Coherence Benefit**: Avoids abrupt mid-sentence cuts common in fixed-size slicing. - **Size Control**: Uses token ceilings to prevent oversized chunks. **Why Sentence-based chunking Matters** - **Retrieval Relevance**: Coherent chunks often align better with user question semantics. - **Generation Quality**: Better-formed context reduces fragmented answer synthesis. - **Citation Clarity**: Sentence-level chunk integrity improves source attribution confidence. - **Readability**: Human review and debugging are easier with natural-language boundaries. - **Tradeoff**: Slightly higher preprocessing complexity than fixed slicing. **How It Is Used in Practice** - **Sentence Packing**: Aggregate adjacent sentences until token threshold is reached. - **Boundary Safeguards**: Preserve headings and bullet context around sentence groups. - **Evaluation Tuning**: Adjust max tokens and overlap by document style and query patterns. Sentence-based chunking is **a strong default for many text-centric RAG systems** - preserving sentence integrity usually improves retrieval precision and grounded response quality over naive fixed-length splitting.