Author Archives: Jesse Liberty
AI Reasoning and Planning
Until very recently, it was observed that LLMs had a very hard time with complex problems. Context was lost, memory of previous steps was distorted, and so forth. This led to unreliable results (hallucinations) and, consequently, to a lack of … Continue reading
PEAS for Agent AI
A classic AI framework to define an agent’s task environment is PEAS. It stands for:
The R in RAG
In my previous post we looked at saving to the vector store. In this short post we’ll look at retrieving that information. The simple search is a good starting point and depends on writing a good prompt, but we can … Continue reading
Deeper into RAG
In the previous post we walked through creating a RAG example, line by line. Let’s take a closer conceptual look at the steps involved in creating a RAG
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RAG In Detail
In my previous post I walked through a RAG example but glossed over the details. In this post I’ll back up and walk through the program line by line. The key steps in RAG are
RAG – A Quick Example
In the previous blog post, we imported a few Python modules and configured our AI key, using Colab. In this blog post we’ll use Retrieval-Augmented Generation (RAG) to extend an LLM that we’ll get from OpenAI. I’ll use a number … Continue reading
Creating Our Python AI Project
As noted in a previous blog post, we’ll be building our project on two platforms: Python and .NET (C#). For Python, we’ll build on Colab. For now, you can use a free account. The first step is to get an … Continue reading
Mads Torgersen
Mads (Lead Designer of C#) joins me to discuss C# and AI as well as what to expect in C# 15. PodcastVideo
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Distributed Computing & Docker
Joe Dluzen joins me to discuss, in depth, distributed computing and Docker. The podcast is here and the video is here.
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Microsoft Agent Framework – Part 0
I’ve been looking at a number of different ways to build Agents. I’ve settled on two and will be documenting what I learn as I go: The advantage of the first is that you understand the underlying mechanisms in more … Continue reading
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AI: The Near Term
As promised, I’ll be posting slides and commentary from my recent user-group presentation on AI (Boston Code Camp). One of my first slides talked about the near-term evolution of AI, defined as either 1-2 years or 6 months, depending on … Continue reading
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MCP In Depth
In a special videoCast, Lance McCarthy of Progress Software dives deep into MCP, not only explaining what it is for and how it works, but demonstrating, in code, how it is done. MCP (Model, Context, Protocol) is an open standard … Continue reading
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