Today I attended and spoke at the 37th Boston Code Camp. The rainy weather was just enough to maximize attendance.
There was an incredibly energetic group of inquisitive people at my talk which was on how you can give your AI LLM a goal and some tools and let it figure out how to move ahead! Lots of questions came from this highly engaged group.
The details of my talk follow.
Building an AI Agent with Semantic Kernel
The classic approach to managing complexity is through abstraction. While also useful in the physical world (you can know how to use a “car” without needing to know about all the parts under the hood), it is an essential tool in software.
To program against the current generative AI models you can use the model’s native abstraction (their SDK). But there are other options too, one of which is to use Semantic Kernel, an open-source library from Microsoft.
In this talk we will understand the first-class abstractions representable using Semantic Kernel, from the granular Function and building up to an Agent, and a couple of steps in between.
This talk will be a mix of explaining AI-relevant and Semantic Kernel-relevant topics + some explanatory sample code. We may also sneak in a little Prompty.
By the end of this talk you will appreciate why you might (or might not) want to build your AI solution with Semantic Kernel (SK) and how you would approach it.
This talk will assume you have used LLMs (like ChatGPT or others) and know the very basics of iterating on prompts and experiencing that GenAI systems have an ability to make decisions from human language. Anything beyond this will be explained in the talk.
The sample application used in the talk can be found here:
https://github.com/semantickerneldev/icon-agent
The deck used in the talk can be found here:
