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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

Cutting a Document Into Chunks

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The pipeline starts with a document that is far too long to hand to the model in one piece. So it gets cut into chunks, consecutive slices of text stacked one after another.
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A long document cannot be handed to the model whole, so it is split into consecutive chunks. A naive cut at a fixed length can land in the middle of a sentence, leaving one chunk with a fragment that no longer carries its meaning and the next chunk with the rest. Overlapping consecutive chunks — repeating a little of the previous chunk at the start of the next — keeps any sentence or idea whole inside at least one chunk.

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