Hands on Let's say you're tasked with solving a math problem like 4,242 x 1,977. Some of you might be able to do this in your head, but most of us would probably be reaching for a calculator right about now, not only because it's faster, but also to minimize the potential for error.
As it turns out, this same logic applies to large language models (LLMs). Ask a chatbot to solve that same math problem, and in most cases, it'll generate a plausible but wrong answer. But, give that model its own calculator and, with the right programming, suddenly it can accurately solve complex equations.
These tools are one of the building blocks to achieving what folks have taken to calling 'agentic AI.' The idea is that given the right tools, AI models can break down, plan, and solve complex problems with limited to no supervision.
And so in this hands on, we'll be exploring some of the ways tool-calling can be used to augment the capabilities and address the limitations of LLMs.
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