TEXT CHUNKING STRATEGIES FOR RAG
The Art of Splitting Documents for Optimal Retrieval
By UDDIT, RAG Optimization Expert & AI Engineer
WHY CHUNKING MATTERS
Proper chunking is critical for RAG system performance. The right strategy can improve retrieval accuracy by 50%+ and significantly reduce costs. Poor chunking leads to context loss, irrelevant retrievals, and degraded response quality.
✎ EDITOR'S NOTE
"Chunking is often overlooked, but it's the difference between a good RAG system and a great one."
FIXED-SIZE CHUNKING
Method: Split by character/token count
Size: 512-1024 tokens typical
Overlap: 10-20% recommended
Best for: General use, simple documents
ADVANCED STRATEGIES
Semantic Chunking
Split based on content meaning and topic boundaries using embeddings similarity.
Best for: Maximum retrieval accuracy
Recursive Splitting
LangChain's RecursiveCharacterTextSplitter. Tries multiple separators hierarchically.
Best for: Preserving document structure
Document-Aware Chunking
Respects headings, paragraphs, lists. Keeps related content together.
Best for: Technical docs, manuals
CHUNK SIZE GUIDE
| Small (256) | Precise, more chunks |
| Medium (512) | Balanced default |
| Large (1024) | More context |
OVERLAP STRATEGIES
OVERLAP RECOMMENDATIONS
- ■ 10% overlap: Minimal, faster processing
- ■ 20% overlap: Recommended balance
- ■ 30% overlap: Maximum context preservation
- ■ Sentence-aware: Don't break mid-sentence
IMPLEMENTATION TIPS
- ✓ Test multiple chunk sizes
- ✓ Measure retrieval accuracy
- ✓ Consider document type
- ✓ Monitor cost vs quality
- ✓ Use metadata enrichment
WHY HIRE ME?
- ✓ RAG optimization specialist
- ✓ 50%+ accuracy improvements
- ✓ LangChain & LlamaIndex expert
- ✓ Production RAG experience
- ✓ NIT Jaipur AI/ML graduate
☎ CONTACT THE AUTHOR
udditalerts247@gmail.com
uddit.site
★ OPTIMIZE YOUR RAG SYSTEM WITH EXPERT CHUNKING ★