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RAG Explained: Retrieval-Augmented Generation From Ingestion to Cited Answer
Retrieval-augmented generation answers questions by retrieving relevant passages from a corpus and giving them to a language model as context, so the model can use inspectable sources. Freshness, permissions and faithful use still need enforcement and evaluation. This guide walks through ingestion, retrieval, augmentation and generation, the design choices at each stage, the common failure points and how to measure each.
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