A public learning journey documenting my progress in AI Engineering.
This repository contains my daily progress as I learn the foundations of modern AI systems, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, prompt engineering, and related technologies.
The objective of this challenge is not only to learn how to use AI tools, but also to understand the concepts behind them and build practical applications throughout the process.
My goal is not just to use AI tools, but to understand how modern AI systems work under the hood and build practical applications with them.
- Understand Large Language Models (LLMs)
- Learn Prompt Engineering
- Build applications using AI APIs
- Learn Retrieval-Augmented Generation (RAG)
- Explore AI Agents and Workflows
- Develop practical AI projects
| Day | Topic | Notes |
|---|---|---|
| Day 01 | Python Fundamentals Revision | Open |
| Day 02 | First LLM API Calls | Open |
| Day 03 | How LLMs Work | Open |
| Day 04 | Message Structure in LLM | Open |
| Day 05 | Prompt Engineering | Open |
| Day 06 | Chain of Thoughts | Open |
| Day 07 | Advanced Prompting | Open |
| Day 08 | Project 1 | Open |
| Day 09 | Prompt Evaluation | Open |
| Day 10 | Context Management | Open |
| Day 11 | Chatbot | Open |
| Day 12 | Prompt Patterns | Open |
| Day 13 | APIs JSON | Open |
| Day 14 | Multiturn Pattern and State Management | Open |
| Day 15 | Project 2 | Open |
| Day 16 | Python for AI | Open |
| Day 17 | SDKs and Ollama | Open |
| Day 18 | RAG | Open |
| Day 19 | Embeddings | Open |
| Day 20 | Chroma DB | Open |
| Day 21 | LangChain | Open |
| Day 22 | Project 3 | Open |
| Day 23 | Improving RAG | Open |
| Day 24 | Advance RAG Patterns | Open |
| Day 25 | Intro to Agents | Open |
| Day 26 | Building Agent | Open |
- Python
- Gemini API
- Git & GitHub
- VS Code
I share my learning progress publicly throughout the journey.