Welcome to the roadmap for learning Generative AI and Large Language Models (LLMs)! This guide will help you explore the exciting field of AI generation step by step, starting from the basics and gradually advancing to more complex topics.

Start by learning the basics of Python programming. It's the foundation for AI development and is relatively easy to grasp. (source: refer to my learning repository.)

Set up your development environment using tools like Google Colab, VSCode, or Jupyter Notebook. These tools make coding and experimenting with AI models easy and accessible.

Understand the core concepts behind Generative AI and Large Language Models. Don't worry; it's not as complicated as it sounds!

(Sources: https://medium.com/google-cloud/generative-ai-learning-path-notes-part-1-d36bc565df1f, https://medium.com/google-cloud/generative-ai-learning-path-notes-part-2-78a1855f6bd0)

Explore the Hugging Face ecosystem and popular open-source libraries like Transformers. These tools make working with Large Language Models much simpler.

Get familiar with the LangChain framework. It's a handy tool for building applications with Large Language Models and doing cool things with natural language.

Put your knowledge into practice by writing your first Generative AI code. It's like bringing your ideas to life!

Master the art of crafting effective prompts to interact with Large Language Models. It's like giving instructions to a smart robot.

Explore Streamlit, a user-friendly library for creating interactive web applications. It's perfect for showcasing your Generative AI projects.

Experiment with different use cases of Generative AI, like text generation or image synthesis. It's like unleashing your creativity!

Dive into embeddings and vector databases. They help AI models understand and manage complex data better.

Learn about Retrieval-Augmented Generation (RAG) and build applications that combine generation with retrieval for better results.

Optimize your RAG-based applications to make them faster and more efficient. It's like fine-tuning a musical instrument to get the perfect sound!

Explore advanced features of the LangChain framework to take your Generative AI projects to the next level.

Understand the basics of fine-tuning Large Language Models for specific tasks. It's like customizing a tool to fit your needs perfectly.

Apply your knowledge of LLM fine-tuning to customize models for tasks like sentiment analysis or text summarization. It's like tailoring a suit to fit just right!

Integrate your Generative AI models into full-stack projects with diverse use cases. It's like building something amazing from start to finish!

Deepen your understanding of Large Language Model architectures, like attention mechanisms and transformer networks. It's like peeking under the hood of a powerful machine.

Learn to deploy and scale your Generative AI applications using cloud platforms like AWS or Google Cloud. It's like having unlimited computing power at your fingertips!

Engage with the AI community through forums, contribute to open-source projects, and attend conferences. It's a great way to learn from others and share your knowledge!

Keep exploring and learning as the field of Generative AI and Large Language Models evolves. There's always something new to discover!

- Learn Python Basics 🐍
- Setup Development Environment 💻
- Learn Generative AI and LLM Fundamentals 🧠
   - Understand Hugging Face Open-source LLM and Libraries 🤗
   - Learn LangChain Framework Fundamentals 🔗
   - Write First Generative AI Code ✍️
- Learn Prompt Engineering 🚀
- User Interface: Learn Streamlit Fundamentals 🎨
- Build Generative AI Apps with Different Use Cases 🛠️
- Understand Embeddings and Vector Database 📊
- Develop Generative AI App with RAG (Retrieval-Augmented Generation) 🤖
   - Improve Performance of RAG 📈
- Learn Advanced Topics of LangChain 🔍
- Learn Fundamentals of LLM Fine-tuning 🎯
   - Fine-tune LLM for Specific Task 🛠️
- Develop Full-stack Projects with Different Use Cases 🌐
- Learn Advanced Topics Related to LLM Architecture 🏗️
- Develop Generative AI App Using Cloud ☁️
- Participation in the AI Community 🤝
- Continuous Learning 📚