Welcome to LearnAI-examples — a practical learning repository designed to help experienced developers get into the world of Artificial Intelligence (AI), with a focus on Generative AI, custom models, and language-based applications.
The goal of this project is to learn AI step-by-step through hands-on exercises, each focusing on a core concept:
- Understanding and using generative models
- Working with text embeddings
- Building document-aware chatbots
- RAG (Retrieval-Augmented Generation)
- Fine-tuning existing models
- Creating agents that can plan and execute tasks
Each exercise is placed in a separate folder with its own code, documentation, and requirements.txt.
| Exercise | Topic | Description |
|---|---|---|
01-pdf-chatbot |
Document Q&A | A chatbot that reads and answers questions based on PDF content using free local models |
02-rag-bot |
Full RAG Pipeline | A bot that performs retrieval + generation from external sources |
03-custom-agent |
AI Agent | An agent that plans and executes tasks based on instructions |
04-summarization-agent |
Text Summarization | An agent that extracts and summarizes web articles using semantic chunking |
- Python 3.10+
- Install dependencies via pip:
pip install -r requirements.txt - No GPU required for the first exercises — models used are lightweight and free to run on CPU
- Hugging Face Transformers
- Sentence-Transformers
- Free and open-source models such as
google/flan-t5-xl,all-MiniLM-L6-v2,sshleifer/distilbart-cnn-12-6
- Experienced developers (Python/Backend/Frontend)
- Engineers who want to get hands-on with AI and LLMs
- No prior ML experience required — all exercises start from scratch with explanations
Feel free to open issues, suggest new tasks, or submit pull requests if you want to contribute to this learning journey.
Happy learning!
Netanel Baba