In a RAG system, document chunking is the process of breaking large texts into smaller, manageable segments ("chunks") before indexing them in a retriever. Effective chunking directly impacts retrieval accuracy, context quality, and overall generation performance.

Poor chunking can lead to fragmented knowledge, irrelevant matches, or context loss, while smart chunking helps the system return relevant, self-contained passages for grounded generation.

📦 Why Chunking Matters

• Improves Retrieval Granularity: Smaller chunks ensure finer matching resolution.
• Reduces Noise: Prevents irrelevant sections from being included in generation.
• Boosts Semantic Precision: Embeddings are more accurate when the input is cohesive.

🔧 Best Practices for Chunking

• Chunk Size: Use chunk sizes between 200–500 tokens as a general guideline. This balances semantic completeness with retrievability.
• Chunk Overlap: Apply 10–20% overlap between adjacent chunks to preserve context flow across boundaries.
• Semantic Boundaries: Prefer splitting at paragraph or section boundaries rather than fixed lengths.
• Metadata Tagging: Include source document name, section headers, or timestamps in metadata to help with traceability and citation.
• Language-Aware Splitting: Use sentence segmentation for natural boundary preservation in multi-lingual content.

🧪 Chunking Strategies

• Sliding Window: Fixed-size chunks with consistent overlap. Simple and effective for most use cases.
• Hierarchical Chunking: Split by document structure (e.g., section → paragraph → sentence) for context-aware layers.
• Semantic Chunking: Use embedding similarity or LLM-based methods to detect topic shifts and natural split points.

📘 Tooling Support

• LangChain TextSplitters: Includes RecursiveCharacterTextSplitter, TokenTextSplitter, Markdown-aware splitters, etc.
• LlamaIndex: Has context-aware and intelligent chunking modes with node-level metadata.
• Haystack & Hugging Face: Offer preprocessors for sentence and paragraph-level chunking with overlap controls.

📌 Example

Original Document: 1800 words / ~2500 tokens technical report

• Split into 6 chunks of ~400 tokens
• Each chunk includes a 50-token overlap with the previous one
• Metadata attached: title, section header, creation date

⚠️ Common Pitfalls

• Overlapping Too Much: Increases index size and retrieval noise
• Chunking Mid-Sentence: Leads to incoherent or confusing embeddings
• No Metadata: Makes evaluation, filtering, and grounding harder

🚀 Summary

Chunking is foundational to an effective RAG pipeline. Smart chunk sizes, overlapping strategies, and semantically-aligned splits ensure that retrievers return high-quality, context-rich data—resulting in better grounded and accurate generation.