In an earlier post I wrote about using Semantic Kernel to create an Agentic AI solution, all using C#. Of course, similar flows can be created with Python. To try this, I’ve created a sample solution to update a resume so it’s more likely to pass the ATS requirements used by various companies nowadays.
My sample is heavilly inspired by Gian Paolo Santopaolo his CV-Pilot repository, which I was not able to use due to the CrewAI tooling phoning home and my DNS (PiHole) blocking those requests.
Read more →We are finally at a state in the GenAI-space where we can create agentic AI solutions with ease.
I’m most familiar with Semantic Kernel, when working with LLMs, and this library works great for creating these solutions.
In a nutshell, what you need to do is create a group chat, add your agents to it, and then let them work together to solve a problem.
Do keep in mind, at the time of this writing, version 1.
Read more →I’m very happy GitHub Copilot exists and lately with the Agent-mode it’s even better. It’s making sure I can focus on the relevant pieces of my solutions and not have to worry too much about the plumbing part.
The models it’s using are quite powerful and contains a lot of (old) data. When using new libraries or versions of already existing libraries, the LLMs used under the hood often don’t provide useful suggestions or edits.
Read more →The current large language models, like GPT-4, GPT-4 Turbo and GPT-4o are great when you need some output generated based on data you feed in the prompt. Even the small language models, like Phi-3, are doing a great job at this. However, these models often don’t know a lot about the data within your company. Because of this, they can’t do a good job at answering questions that required data from your organization.
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