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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
Why the Model Cannot Answer From Your Documents Alone

Closed Book Versus Open Book

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That grey zone is exactly the problem retrieval-augmented generation solves. Think of the plain model as a closed-book exam: it answers only from what it memorized, and your document was never on the study list.
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Retrieval-augmented generation changes the order of operations. Instead of asking the model to answer from memory, the system first searches the document collection for the passages most relevant to the question, then places those passages next to the question and lets the model read them before answering. The model still generates the words, but now the evidence is in front of it.

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