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Chunking

Chunking (frammentazione)

Chunking is the practice of splitting long documents into smaller, manageable pieces, the "chunks", before indexing them for retrieval. You cannot hand a model an entire thousand-page manual to answer a question: you break it into coherent paragraphs or sections, each short enough to fit the context yet rich enough to make sense alone. It is like cutting a long film into scenes: seeking a precise moment, you retrieve the right scene instead of the whole movie. The challenge is where to cut: chunks too small lose context, too large dilute relevance and waste space.

Definition

Chunking matters because it is one of the choices that most affect a RAG system's quality. Well-done chunking, perhaps with overlaps between pieces so concepts are not split, ensures retrieval finds exactly the useful information instead of incomplete or irrelevant fragments.

Related terms

• Retrieval
• Embedding
• Vector database
• RAG (Retrieval Augmented Generation)
• Context Window

More in AI Fundamentals

• LLM (Large Language Model)
• Token
• Context Window
• Temperature
• AI Hallucination
• Fine-tuning
• RAG (Retrieval Augmented Generation)
• Embedding

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