I mentioned on linkedin that I thought it would be a fun diversion to build a PyTorch model to forecast the US Presidential Election. This was an interesting project for a few reasons. At the time I did this, it was about two weeks before the presidential election in the United States. As I write … Continue reading →

Retrieval-Augmented Generation (RAG) is a powerful technique for enhancing Large Language Models (LLMs) with custom, up-to-date information. Integrating RAG into LLM workflows allows organizations to leverage their proprietary data to generate more accurate, relevant, and contextually appropriate responses. This approach bridges the gap between the LLM’s pre-trained knowledge and specific. This includes confidential information that … Continue reading →

The more I work in Langchain, the more I discover its strength for creating powerful, custom Large Language Model interactive AI powered chatbots. For example, right now I am using it to create a custom chatbot that is trained on our proprietary internal documentation, reports, and log files. This uses something called RAG (Retrieval Augmented … Continue reading →

As many of you know, I have been building a personal AI assistant using Large Language Model transformers. The goal is to have it access documents and internet feeds to take care of different tasks. This week I have been experimenting with Meta’s newest iteration of their open source transformer, Llama3. I downloaded their 8B … Continue reading →

Over the next serveral weeks I will be diving into my work in creating a personal assistant, much like a private chatGPT. The main reason will be so I can explore the different capabilities of LangChain and how I can use it to do exploratory data analysis on my library of PDF files, emails, and … Continue reading →

All work and no play, right? Yet, I’ve found that setting time aside to play can be rewarding, especially when it comes to doing something that lets me learn and gain a deeper appreciation and understanding of my coding. Ever thought about how empowering it feels to be a creator? Even though I’ve been working … Continue reading →

When I chose my final senior thesis and project for my computer science degree I chose to write an artiificial intelligence based model that used a custom machine learning code that I trained to predict future housing prices based on macroeconomic data. I wanted to dive more into machine learning and what sorts of insights … Continue reading →

The thought of machines as our coworkers brings to mind futuristic images of robots sitting in the cubical next to us and standing next to us on the bus going to work. But in reality, we are working alongside robots already, in the computer, as apps. What does this have to to with us as … Continue reading →