RAG Chunking Previewer
Visualize how your text is split for vector indexing. Perfect for tuning RAG retrieval quality.
Configuration
Visualizer (7 Chunks)
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Large Language Models (LLMs) are revolutionary tools that have transformed the way we process inform
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ay we process information. However, they have limited context windows. To solve this, we use RAG (Re
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this, we use RAG (Retrieval-Augmented Generation). The first step in RAG is chunking. Chunking is th
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king. Chunking is the process of breaking down large documents into smaller, more manageable pieces
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e manageable pieces called 'chunks'. This allows the model to process specific information without e
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nformation without exceeding its token limit. Effective chunking involves balancing chunk size and o
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ing chunk size and overlap to maintain semantic meaning while ensuring efficient retrieval.
Pro Tip: The Overlap Strategy
Overlap is critical for preserving context between chunks. Without overlap, a sentence split across two chunks might lose its meaning. A standard starting point is **10-20% overlap**. For technical documentation or code, higher overlap often yields better retrieval accuracy.