Stories
Demystifying AI: Understanding LLM, RAG, AI Agents and MCP
- •LLMs like ChatGPT act as the core conversational engines that generate responses based on training data.
- •RAG enhances accuracy by allowing AI to search external documents and databases before generating an answer.
- •AI Agents take productivity further by actively executing tasks and interacting with tools with user permission.
- •MCP serves as a standard protocol connecting AI applications with external tools, services, and data sources.
- •Together, these technologies work in synergy where LLMs generate, RAG retrieves, Agents execute, and MCP connects.
Key Questions Answered
An LLM generates responses using its training data and conversation context, acting like someone who has read many books. In contrast, RAG allows an AI system to search specific external documents, databases, or HR policies to retrieve accurate information before generating an answer.
While regular AI assistants only suggest or tell you how to do something, an AI Agent can actually take steps toward performing tasks. With proper tools and user permission, an agent can search for flights, compare prices, check availability, and handle parts of a booking process.
MCP is a standard that helps AI applications connect with external tools and information sources. It does not generate answers or make decisions on its own, but instead provides a common way for compatible AI systems to securely access required data and applications.
