If you are attending this tutorial you will need a laptop with a browser and a GitHub account. The tutorial can be entirely completed using GitHub Codespaces, a free online development environment.
If you would prefer to run everything on your own machine you will need a Python 3.9 or higher local development environment with the ability to create a virtual environment and install packages using pip.
You can pre-install the packages we will be using like this:
git clone git@github.com:davoshack/building-with-llms-training-session-2.git
cd building-with-llms-training-session-2
# Optional if you want a virtual environment (no need to do this on Codespaces):
python -m venv venv
source venv/bin/activatecd agentic-patterns
pip install -r requirements.txt
cd fastapi-for-ai-applications
pip install -r requirements.txtcd langchain-fundamentals
pip install -r requirements.txtLarge Language Models such as GPT-4o, Claude and Google Gemini provide APIs that can be used to develop features that were almost impossibly difficult to build in the past, spanning areas that include human language understanding, image recognition and structured data extraction.
Building software that uses these APIs reliably and responsibly is a topic with a great deal of depth and a lot of hidden traps.
In this second session, we'll explore the fundamentals of the RAG Architecture, Agent Patterns and Architectures for AI Applications
Topics we will cover include: