The article explores the differences between two AI model approaches, Retrieval-Augmented Generation (RAG) and Model Context Protocol (MCP), highlighting their distinct capabilities and applications. RAG is designed to enhance the responses of large language models (LLMs) by retrieving relevant unstructured data and injecting it into prompts as additional context, making it particularly effective for stable data like company policies. In contrast, MCP is a standardized protocol that allows LLMs to interact with structured external data sources in real-time, enabling them to perform actions such as data retrieval and task automation. The article discusses when to use each approach, noting that RAG is ideal for question-answering over large collections of unstructured data, while MCP is suited for real-time, action-oriented tasks involving structured data. It also suggests that the two approaches can be complementary, with RAG providing contextual understanding and MCP enabling real-time actions, and anticipates future developments where RAG could expand to include multimodal content, and MCP could enhance AI agent autonomy through more complex decision-making and task execution.