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Master AI fundamentals without frameworks. Just Python, APIs, and clarity.
When I started learning AI development, I found endless tutorials on various frameworks and libraries. But when something broke, I had no idea what was actually happening under the hood. I couldn't debug issues, optimize performance, or make informed architectural decisions.
This course fills that gap. It teaches you what AI systems actually do, stripped of abstractions and marketing jargon. You'll learn the foundational patterns that all AI applications use - whether they admit it or not.
Frameworks have their place. I use them. I like several of them. But jumping into a framework without understanding the fundamentals is inefficient. You build on shaky ground.
This course teaches you what's really happening when you:
- Chat with an AI
- Give it memory
- Make it use tools
- Have it search your documents
- Build multi-step workflows
Once you understand these patterns, you can use any framework effectively—or build exactly what you need without one.
The industry loves complex terminology. Here's what things really are:
| AI Agents | Python functions the AI decides to call |
| Memory/Context | A Python list of messages you send with each request |
| RAG (Retrieval Augmented Generation) | Search for relevant text + add to prompt + ask AI |
| Multi-Agent Systems | Sequential API calls with logic between them |
| Embeddings/Vector DBs | Useful for large datasets, but keyword search works fine for most cases |
| Prompt Chaining | Call AI → process result → call AI again |
That's it. No magic. Just API calls and basic programming.
This course covers 7 foundational concepts, each with working code examples for both Anthropic's Claude and OpenAI's GPT:
- Your First AI Call - The basic pattern every AI application uses
- Conversation Memory - How chatbots remember previous messages
- Tool Calling - Making AI take actions through functions
- RAG - Making AI answer questions from your documents
- Conversational RAG - Adding follow-up questions to document Q&A
- Streaming - Displaying AI responses word-by-word in real-time
- Prompt Chaining - Building multi-step AI workflows
Each concept builds on the previous one. By the end, you'll understand how production AI systems work.
You only need to know Python. That's it.
No machine learning background. No advanced math. No framework experience.
If you can write functions, loops, and understand lists and dictionaries, you're ready.
1. Clone this repository
2. Install dependencies
3. Set up your API keys
Copy .env.example to .env:
Add your API keys to .env:
Get API keys:
- Anthropic: https://console.anthropic.com/
- OpenAI: https://platform.openai.com/api-keys
4. Start with the first module
Each module contains:
- A README explaining the concept
- Python examples for Anthropic Claude
- Python examples for OpenAI GPT
- Heavily commented code showing exactly what's happening
Work through them in order:
Learn the foundational pattern: initialize → call → extract response.
Understand how chatbots remember context (spoiler: it's just a list).
Make AI take actions by calling your Python functions.
Make AI answer questions from your own documents.
Add follow-up questions to your document Q&A system.
Display AI responses in real-time, word by word.
Build multi-step AI workflows and "agent" systems.
I'm Max Braglia, and I write about AI engineering at maxbraglia.substack.com. I built this course because I wish it existed when I started learning AI development.
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MIT License - use this code however you want. Learn, build, teach others.
