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How AI Answers Questions About Documents It Never Learned

1Why the Model Cannot Answer From Your Documents Alone2Chunking and Embedding: Turning Documents Into Something Searchable3Retrieval: Finding the Right Passages for a Question4Grounded Generation: Answering From the Retrieved Text
Chunking and Embedding: Turning Documents Into Something Searchable

How Big Should a Chunk Be

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Overlap fixes broken sentences, but it leaves an open question: how much text should one chunk hold? Push the size down, and each chunk becomes a fragment with no surroundings.
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Chunk size is a trade-off. Very small chunks carry too little surrounding context, so a passage that only makes sense together with its neighbours becomes ambiguous on its own. Very large chunks carry so much text that the specific answer is buried among unrelated sentences, diluting what the model reads. A workable chunk size keeps one coherent idea together without dragging in the rest of the document.

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