An architecture that combines a retrieval system with an LLM — the model first retrieves relevant documents, then generates answers grounded in that retrieve...
When the AI looks up real information in a library before answering, instead of just guessing from memory.
A technique where AI searches through actual documents or databases first, then uses what it finds to give you a more accurate answer.
An architecture that combines a retrieval system with an LLM — the model first retrieves relevant documents, then generates answers grounded in that retrieved context.
A hybrid approach pairing a retrieval module (dense or sparse) with a generative model, allowing the system to condition its output on dynamically retrieved evidence, reducing hallucination and enabling knowledge updates without retraining.
A retrieve-then-generate pipeline where a bi-encoder or cross-encoder retriever supplies grounding documents to a conditional language model, decomposing world knowledge into a parametric component (model weights) and a non-parametric component (external index).
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