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.
In depth
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. In coding agents, window sliding lets the model work through large files section by section. In RAG systems, overlapping windows during chunking ensure that no information is lost at chunk boundaries. The overlap size (typically 10-20% of the window) is a key parameter.
Example
In practice, developers reach for Window Sliding when they need the capability described above as part of an AI feature or workflow.
Go deeper at Developers Digest
Hands-on guides, comparisons, and tutorials that cover RAG & Retrieval.
FAQ
What is 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.
Why does Window Sliding matter for AI developers?
Window Sliding 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 Window Sliding?
Developers Digest publishes tutorials and videos that cover RAG & Retrieval topics including Window Sliding. Check the blog and YouTube channel for hands-on walkthroughs.
Related terms
RAG & Retrieval
Chunking
The process of splitting large documents into smaller, overlapping segments for embedding and retrieval in RAG systems.
RAG & Retrieval
Knowledge Cutoff
The date after which a model has no training data.
RAG & Retrieval
Retrieval
The process of finding relevant documents, passages, or data from a knowledge base in response to a query.
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