The article discusses the rise of AI, focusing on Retrieval-Augmented Generation (RAG) as a method to enhance Large Language Models (LLMs). RAG allows LLMs to fetch real-time information from databases or search engines to improve the accuracy and relevancy of their responses. The article then delves into two key methods for implementing RAG: the Model Context Protocol (MCP) and function calling. MCP not only updates the model's knowledge base but also enables it to perform specific actions, facilitating more dynamic applications such as AI scheduling assistants.
MCP is a protocol defined by Anthropic that describes a language or a framework via which we can connect an LLM model with the external world.
RAG is a technique that augments an LLM's response generation by retrieving information from a source, which can be anything from a database to a search engine.
#ai #retrieval-augmented-generation #large-language-models #model-context-protocol #function-calling
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