Retrieval-augmented generation (RAG) combines large language models (LLMs) with external knowledge retrieval.
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Retrieval-Augmented Generation (RAG) merges LLMs with retrieval systems to boost output quality. Fine-tuning LLMs tailors them to specific needs on given datasets.
Retrieval Augmented Generation (RAG) is a machine learning technique that combines the power of retrieval-based methods with generative models.