Part-1 Master Generative AI and RAG(Retrieval-Augmented Generation) - Theory скачать в хорошем качестве

Part-1 Master Generative AI and RAG(Retrieval-Augmented Generation) - Theory

What it is: RAG combines the strengths of LLMs with information retrieval systems, enabling AI models to access and utilize external knowledge bases. How it works: It works by retrieving relevant information from databases, documents, or web sources and providing it as context for the LLM to generate responses. Benefits: Improved Accuracy: RAG helps LLMs provide more accurate and contextually relevant answers by grounding their responses in external data. Up-to-date Information: It allows LLMs to access and utilize the latest information, rather than being limited to their training data. Reduced Hallucinations: By grounding LLMs in external sources, RAG helps reduce the likelihood of models generating incorrect or fabricated information. Domain-Specific Knowledge: RAG enables LLMs to access and utilize domain-specific knowledge from internal or external sources.