This repository offers a comprehensive collection of over 500 real-world case studies detailing the production implementation of Generative AI and Large Language Models (LLMs) across more than 130 companies. It serves as a valuable resource for AI/ML engineers, engineering teams, and leaders seeking to understand and implement GenAI solutions, providing insights into architecture, scaling, optimization, and evaluation.

How It Works

The repository curates documented system designs, covering key areas such as Retrieval-Augmented Generation (RAG), fine-tuning, multi-modal systems, and agent-based architectures. It categorizes case studies by industry, use case, and company, offering practical examples of how leading tech firms like Google, Microsoft, and Netflix approach LLM deployment, optimization, and evaluation in production environments.

Quick Start & Requirements

• Access the case studies directly within the repository.
• No specific software installation is required to browse the content.
• Links to official documentation, demos, or community channels are not provided in the README.

Highlighted Details

• Over 500 case studies from 130+ companies, updated as of March 2025.
• Detailed breakdowns by industry (Tech, E-commerce, Media, etc.) and use cases (RAG, Fine-tuning, Evaluation).
• Features prominent companies like OpenAI, Google, Meta, and Netflix.
• Illustrates common GenAI architectures such as RAG, Fine-tuning, and Multi-Agent Systems.

Maintenance & Community

• The repository is actively maintained, with the last update on March 08, 2025.
• Contributions are welcomed via pull requests, with guidelines provided in CONTRIBUTING.md.

Licensing & Compatibility

• Licensed under the MIT License, permitting commercial use and integration with closed-source projects.

Limitations & Caveats

The repository focuses on documented case studies and does not provide runnable code, direct links to demos, or specific community channels for discussion or support.