From the course: Mastering Model Context Protocol (MCP)
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Working with LLMs
From the course: Mastering Model Context Protocol (MCP)
Working with LLMs
- [Instructor] So why does MCP matter? Well, to appreciate its value, we need to look at where we started with large language models. At their core, large language models work on probability. They've been trained on massive amounts of text from across the internet, which gives them the ability to generate texts that makes sense based on what you asked them. So think of it this way: If you give a large language model the phrase, "Paris is the capital of...", it can predict that France is the most likely next token, and then keep building from there to create longer sentences. This probabilistic approach made large language models incredibly useful for things like creative writing and drafting emails. Basically, any situation where you need coherent, relevant text. And we started experimenting with them across different domains, running benchmarks, and trying to figure out how to measure this kind of broad capability. However, clear pattern emerged. Task performance was remarkably…
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