Author Archives: Jesse Liberty
Creating a multi-agent application. Part 2
In my previous post, I showed the output of a multi-agent application I wrote to create blog posts (not to worry, it is for demonstration purposes only). In this post, I will begin the process of working through the code, … Continue reading
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Creating a multi-agent application – Part 1
The following text was created by a multi-agent application designed to create blog posts. In my next post we’ll take the application apart, step by step. For now, here is a test run with the prompt Use of multiagents in … Continue reading
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ReAct and Agents in AI
In the previous post, we looked at the use of Chain of Thought (CoT) reasoning in the context of LLMs. For an LLM to take action in the world, however, it needs agents. The paradigm for this is called ReAct—that … Continue reading
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
Posted in Agents, AI, Essentials
Tagged Agentics, AI, Chain-of-thought
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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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