Learn effective chunking strategies including fixed-size, semantic, recursive, and sentence-window approaches for optimal RAG retrieval.
What are the best chunking strategies for RAG systems?
Chunking determines how documents are split for embedding. Fixed-size chunks are simple but may break semantic units. Semantic chunking splits at natural boundaries. Recursive chunking tries multiple separators hierarchically. Sentence-window chunking embeds sentences but retrieves surrounding context. Most systems use 256-1024 tokens with 10-20% overlap.
Chunking is one of the most critical decisions in RAG system design. Poor chunking leads to:
• **Lost context** — Important information split across chunks
The goal is to create chunks that are self-contained, semantically coherent, and appropriately sized for your embedding model.
Split text into fixed token counts (e.g., 512 tokens) with overlap (e.g., 50 tokens).
**Advantages:**
**Disadvantages:**
Use overlap to mitigate boundary problems. 10-20% overlap is typical. This is a good baseline approach, especially for homogeneous content like documentation.
Split at natural boundaries: paragraphs, sections, or detected topic changes.
**Approaches:**
**Advantages:**
**Disadvantages:**
Sentence-Window Chunking — Embed individual sentences but retrieve surrounding sentences for context. Improves retrieval precision while maintaining context for generation.
Parent-Document Retrieval — Store both chunks and full documents. Retrieve based on chunks but return parent documents to the LLM. Best of both worlds for precision and context.
Hierarchical Chunking — Create chunks at multiple granularities (document summary, section, paragraph). Retrieve at the appropriate level based on query type. More complex but highly effective.
Recursive Character Splitting — LangChain's approach: try multiple separators hierarchically (headers, paragraphs, sentences, characters). Flexible and works across content types.
Chunk size impacts multiple factors:
• **Retrieval precision** — Smaller chunks = more precise matching
**Recommendations:**
Different content types may warrant different strategies. Don't be afraid to experiment and measure.
Compare Pinecone, Weaviate, Qdrant, pgvector, and Chroma to find the right vector database for your RAG implementation.
Techniques to minimize LLM hallucinations in RAG including better retrieval, prompt engineering, verification, and UX design.
Measure RAG quality with retrieval metrics, generation evaluation, and end-to-end assessment using RAGAS and custom benchmarks.
Explore more RAG implementation topics
Back to RAG AI Knowledge SystemsBased in Bangalore, we help enterprises across India and globally build RAG systems that deliver accurate, citable answers from your proprietary data.
We design document pipelines, chunking strategies, and embedding approaches tailored to your content types and query patterns.
Our RAG systems include hallucination detection, confidence scoring, source citations, and proper error handling from day one.
We implement access control, PII handling, audit logging, and compliant deployment for sensitive enterprise data.
Share your project details and we'll get back to you within 24 hours with a free consultation—no commitment required.
Boolean and Beyond
825/90, 13th Cross, 3rd Main
Mahalaxmi Layout, Bengaluru - 560086
590, Diwan Bahadur Rd
Near Savitha Hall, R.S. Puram
Coimbatore, Tamil Nadu 641002