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Retrieval augmented generation is the technique of letting a language model look things up before it answers. The system searches a collection of documents, pulls the passages that best match the question, and hands them to the model as context, so the answer is grounded in retrieved text rather than in memory alone.
A model on its own knows only what was in its training data, cannot cite a source, and will produce a confident answer whether or not it has the facts. Retrieval fixes all three at once: the information can be current, it comes with a link, and the model is working from text in front of it rather than recollection.
Whole pages are rarely retrieved. Documents are split into passages of a few hundred words, and those passages are matched against the question individually. A page is therefore not competing as a page. Each section of it competes on its own, against sections of other pages.
A chunk that opens with a pronoun, or depends on a heading two screens up for its subject, is unusable once separated from the page. A chunk that names the subject and answers immediately survives the trip. This is the single mechanical reason behind every writing rule in this set.
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