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Giant language fashions normally give nice solutions, however as a result of they’re restricted to the coaching information used to create the mannequin, over time they’ll develop into incomplete–or worse, generate solutions which can be simply plain improper. A technique of bettering the LLM outcomes known as “retrieval-augmented era” or RAG. On this video, IBM Senior Analysis Scientist Marina Danilevsky explains the LLM/RAG framework and the way this mix delivers two large benefits, specifically: the mannequin will get essentially the most up-to-date and reliable information, and you’ll see the place the mannequin bought its information, lending extra credibility to what it generates.
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