0. Create a new directory with these three files (requirements.txt, main.py, README.md)
1. python -m venv venv
2. source venv/bin/activate
3. pip install -r requirements.txt
4. python main.py
5. Update main() to run the example prompt chains

In response to Dr. Andrew Ng's letter, "Four AI agent strategies that improve GPT-4 and GPT-3.5 performance".

When I read Andrew's letter, I'm imagining him as Steve Balmer, shouting "Agentic, agentic, agentic workflows!". Haha, we can hear you. No need for that.

Before we move on, let's be clear what is agent in this context. The context is, we're now in 2024 and LLMs such as GPT-4 and Llama 3 is the state-of-the-art. In early 2022, everybody in the field knew about the agent from RL, but the general public had no conception of what it was. Their narrative were still everything is a chatbot. All sorts of different things are being called agents. Chatbots are being called agents. Things that make a function call are being called agents. Now when people think agent, they actually think the right thing.

An agent is something that you can give a goal and get an end step workflow done correctly in the minimum number

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.