There is a video version of this post in my YouTube Channel. So we have our boilerplate app, and we are ready to start adding fun stuff. If you do not have that, check the part 1 of this series. We will start by making sure we have Semantic.Kernel in our usings: using Microsoft.SemanticKernel; Now we will configure the “Kernel”, which as the name says is the heart of SK (Semantic Kernel). We will add this to our services configuration helper method:

If you prefer video, there is a YouTube version of this blog-post. Since the release of LLMs, I have been extremely enthusiastic about exploring such a fun tool to play with. I won’t delve into discussions about how transformative it is or will be, but rather focus on the enjoyable aspect of using this technology. In the broader context of ML/AI and data science, Python stands out as the most commonly used programming language. Despite my open-source enthusiasm, I have always found C# to be remarkable. With the introduction of Mono.NET and later dotnet core, I have happily used it in Linux environments. It’s no surprise that when working on projects related to ML/AI, C# and .NET are my preferred choices.

ChatGPT and most AI systems have biases, often manifesting as racism and discrimination, mirroring the biases present in human society and hence in the data we use to train such systems. And look, I am excited about AI, but sometimes we are moving too fast without thinking in the consequences. Last week we had our team monthly lunch, and as in other ocassions we were talked about everything, ranged from topics as cane sugar industry, spicy food levels, skiing, and of course, AI. The small talk was about tips on using Microsoft Teams and Copilot to summarize what people said in a chat after prolonged absences from the office. This sparked an idea for an experiment.