MCP Sampling Fundamentals of Sampling
Smart Sampling: The Secret Weapon in Modern AI’s Toolkit
Imagine training an AI model by showing it every possible example in existence. Sounds thorough, right? It’s also completely impractical. Even the tech giants with their massive compute resources would buckle under the sheer volume of data. This is where the art and science of sampling comes in—the strategic selection of which data points, which human feedback, and which evaluation scenarios will teach your AI model the most. This concept of strategic sampling sits at the heart of the Model Context Protocol (MCP), a framework designed to standardize how AI systems access data, execute actions, and improve through feedback.