RAG & Retrieval
Chunking
The process of splitting large documents into smaller, overlapping segments for embedding and retrieval in RAG systems.
In depth
The process of splitting large documents into smaller, overlapping segments for embedding and retrieval in RAG systems. Chunk size and overlap strategy directly affect retrieval quality. Too large and you lose precision. Too small and you lose context. Common strategies include fixed-size chunks (500-1000 tokens), sentence-based splitting, and recursive character splitting that respects document structure like headings and paragraphs.
Example
Common strategies include fixed-size chunks (500-1000 tokens), sentence-based splitting, and recursive character splitting that respects document structure like headings and paragraphs.
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FAQ
What is Chunking?
The process of splitting large documents into smaller, overlapping segments for embedding and retrieval in RAG systems.
Why does Chunking matter for AI developers?
Chunking sits in the RAG & Retrieval part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Chunking?
Developers Digest publishes tutorials and videos that cover RAG & Retrieval topics including Chunking. Check the blog and YouTube channel for hands-on walkthroughs.
Related terms
RAG & Retrieval
Retrieval
The process of finding relevant documents, passages, or data from a knowledge base in response to a query.
RAG & Retrieval
Window Sliding
A technique for processing text that exceeds a model's context window by moving a fixed-size window across the input, processing each chunk, and combining the results.
RAG & Retrieval
Data Pipeline
A sequence of automated steps that move and transform data from source to destination.
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