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Learning Generative AI from scratch with step-by-step Google Colab notebooks. Build scalable architectures for enterprise-level solutions, starting with the basics of RAG systems.

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GenAI: From Basics to Production

Overview

This repository is a comprehensive learning resource to guide you through developing Generative AI (GenAI) solutions. Starting from foundational concepts, you'll progress to building scalable architectures for enterprise-level use cases like querying distributed domain knowledge.

Examples of potential applications:

  • IT knowledge management
  • Product specifications for retailers
  • Internal employee portals

By the end of this journey, you'll have the skills and confidence to create robust Generative AI systems.


Key Features

  • Beginner-Friendly: Start from the basics of Generative AI and build up to advanced topics.
  • Practical Notebooks: Hands-on exercises using Google Colaboratory.
  • Scalable Architectures: Learn to design solutions for real-world, enterprise-level challenges.
  • Customizable: Adapt the provided solutions to your specific use cases.

Repository Structure

├── data/                     # Add your domain-specific datasets here.
├── notebooks/                # Step-by-step Google Colaboratory notebooks.
├── solutions/                # Completed versions of notebooks for reference.
├── README.md                 # Documentation for the repository.

Requirements

Technical Requirements

  • Laptop with Internet Access
  • Google Account (required for Google Colaboratory)

If you don’t have a Google account, create one for free:
Create Google Account

Tools Used

  • Google Colaboratory (Colab): A cloud-based tool to execute Python code seamlessly.

Getting Started

Clone the Repository

git clone https://github.com/your-username/genai-from-basics-to-production.git
cd genai-from-basics-to-production

Set Up the Environment

  1. Open any notebook from the notebooks/ folder in Google Colaboratory.
  2. Add your datasets to the data/ folder or upload them directly to Colab as needed.

Follow the Notebooks

  • Start with the introductory notebooks and progress to more advanced topics.
  • Each notebook includes clear explanations, practical code snippets, and exercises to deepen your understanding.

Contributing

We welcome contributions from the community!

  • If you have suggestions, enhancements, or bug fixes, feel free to open an issue or submit a pull request.

License

This project is licensed under the Apache 2.0 License. For more details, see the LICENSE file.


Acknowledgments

This project leverages Google Colaboratory and popular open-source libraries to provide an accessible, practical learning experience for everyone.

Happy learning! 😊

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Learning Generative AI from scratch with step-by-step Google Colab notebooks. Build scalable architectures for enterprise-level solutions, starting with the basics of RAG systems.

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