Chunking
Breaking large documents or texts into smaller, manageable pieces for processing. Critical for RAG systems where documents must fit within context windows.
In-Depth Explanation
Chunking is the process of dividing large documents into smaller segments for processing by AI systems. It's a critical step in RAG pipelines where chunk quality directly impacts retrieval and answer quality.
Why chunking matters:
- Models have context window limits
- Smaller chunks enable precise retrieval
- Embedding quality degrades for very long texts
- Allows returning most relevant portions
Chunking strategies:
- Fixed size: Split every N characters/tokens
- Sentence-based: Split at sentence boundaries
- Paragraph-based: Maintain paragraph structure
- Semantic: Use AI to find natural breaks
- Recursive: Hierarchical splitting with overlap
Key parameters:
- Chunk size: How large each piece is (typically 200-1000 tokens)
- Chunk overlap: How much consecutive chunks share (typically 10-20%)
- Separators: What constitutes a break point
Common mistakes:
- Chunks too small: lose context
- Chunks too large: dilute relevance
- No overlap: miss information at boundaries
- Ignoring structure: break mid-sentence/thought
Business Context
Proper chunking strategy can make or break RAG performance. Chunks that are too small lose context; too large wastes tokens and reduces relevance.
How Clever Ops Uses This
We extensively tune chunking strategies for Australian business RAG systems. The right approach depends on content type, query patterns, and retrieval requirements.
Example Use Case
"Splitting a 100-page manual into 500-word chunks with 50-word overlaps for better retrieval in a support chatbot."