🤖 GenAI & LLM System Design: 500+ Production Case Studies

The largest collection of 500+ real-world Generative AI & LLM system design case studies from 130+ companies. Learn how industry leaders design, deploy, and optimize large language models and generative AI systems in production.

First published: June 14, 2023. Last updated: March 08, 2025

📍 Original Source Attribution: The foundational 500 ML case studies in this repository were originally curated by Evidently AI. This repository builds upon their excellent work, expanding with additional GenAI and LLM-focused content and organization.

🔍 Quick Navigation

• What's Inside
• Featured LLM Case Studies
• Browse by Industry
• Browse by Use Case
• Browse by Company
• GenAI Architectures
• Contributing

📚 What's Inside

This repository documents how companies build and deploy production-grade Generative AI and LLM systems, focusing on:

• Architecture decisions for RAG, fine-tuning, and multi-modal systems
• Scaling strategies for billion-parameter models
• Optimization techniques for latency, cost, and performance
• Evaluation frameworks for LLM outputs and hallucination mitigation
• Deployment patterns across industries

Perfect for:

• AI/ML Engineers implementing LLM-powered features
• Engineering teams designing scalable GenAI architectures
• Leaders planning generative AI initiatives
• Technical interviews on LLM system design

🏆 Featured LLM Case Studies

RAG & Knowledge Retrieval

• Ramp: From RAG to Richness: How Ramp Revamped Industry Classification - Enterprise RAG implementation
• GitLab: Developing GitLab Duo: How we validate and test AI models at scale - Testing LLM quality at scale
• Picnic: Enhancing Search Retrieval with Large Language Models - LLM-powered search

GenAI Applications

• Slack: How We Built Slack AI To Be Secure and Private - Enterprise LLM security
• Discord: Developing rapidly with Generative AI - Generative AI platform
• GoDaddy: LLM From the Trenches: 10 Lessons Learned Operationalizing Models - LLM production lessons

📊 Browse by Industry

• Tech (90 case studies) - 24 LLM case studies
• E-commerce and retail (119 case studies) - 21 GenAI case studies
• Media and streaming (44 case studies) - 18 LLM case studies
• Social platforms (57 case studies) - 15 GenAI case studies
• Fintech and banking (31 case studies) - 12 LLM implementations
• Delivery and mobility (108 case studies) - 10 GenAI applications

💡 Browse by LLM/GenAI Use Cases

• LLM implementation (92 case studies)
• Generative AI applications (98 case studies)
• RAG systems (42 case studies)
• LLM-powered search (60 case studies)
• NLP & text processing (48 case studies)
• LLM evaluation (36 case studies)
• Fine-tuning approaches (22 case studies)
• LLM inference optimization (19 case studies)
• Multi-modal systems (17 case studies)
• Content personalization (15 case studies)

🔍 Top Companies with LLM & GenAI Case Studies

• OpenAI (8 case studies)
• Anthropic (7 case studies)
• Microsoft (16 case studies)
• Google (15 case studies)
• Meta (12 case studies)
• Hugging Face (9 case studies)
• Netflix (14 case studies)
• LinkedIn (19 case studies)
• GitHub (7 case studies)
• Spotify (10 case studies)

📚 LLM System Design Patterns

• Pattern 1: Direct LLM Integration

  • Cost-effective for simple use cases
  • Examples: GitHub Copilot

• Pattern 2: RAG (Retrieval-Augmented Generation)

  • Improves accuracy with domain-specific knowledge
  • Examples: Ramp's Industry Classification

• Pattern 3: Multi-Agent Systems

  • Complex reasoning through agent collaboration
  • Examples: AutoGPT-like architectures

• Pattern 4: Human-in-the-Loop

  • Critical applications requiring human oversight
  • Examples: Content moderation systems

📈 LLM Evolution Timeline

• 2023 Q1-Q2: First wave of RAG implementations
• 2023 Q3-Q4: Fine-tuning becomes mainstream
• 2024 Q1-Q2: Agent architectures emerge
• 2024 Q3-Q4: Multi-modal systems gain traction
• 2025 Q1: Real-time personalization with LLMs

🏗️ GenAI Architectures

RAG (Retrieval-Augmented Generation)

┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│                 │     │                 │     │                 │
│  Document       │────▶│  Vector         │     │                 │
│  Corpus         │     │  Database       │────▶│                 │
│                 │     │                 │     │   LLM           │
└─────────────────┘     └─────────────────┘     │   Generation    │
                                                │                 │
┌─────────────────┐     ┌─────────────────┐     │                 │
│                 │     │                 │     │                 │
│  User           │────▶│  Query          │────▶│                 │
│  Query          │     │  Processing     │     │                 │
│                 │     │                 │     │                 │
└─────────────────┘     └─────────────────┘     └─────────────────┘

Fine-tuning Approaches

┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│                 │     │                 │     │                 │
│  Base LLM       │────▶│  Fine-tuning    │────▶│  Specialized    │
│  Model          │     │  Pipeline       │     │  Model          │
│                 │     │                 │     │                 │
└─────────────────┘     └─────────────────┘     └─────────────────┘
                              ▲
┌─────────────────┐           │
│                 │           │
│  Company        │───────────┘
│  Data           │
│                 │
└─────────────────┘

Feature Store for LLMs

┌─────────────────┐     ┌─────────────────┐
│                 │     │                 │
│  Real-time      │────▶│  Feature        │
│  Data           │     │  Computation    │
│                 │     │                 │     ┌─────────────────┐
└─────────────────┘     └─────────────────┘     │                 │
                              │                 │                 │
┌─────────────────┐           ▼                 │                 │
│                 │     ┌─────────────────┐     │                 │
│  Batch          │────▶│  Feature        │────▶│  LLM            │
│  Data           │     │  Store          │     │  Application    │
│                 │     │                 │     │                 │
└─────────────────┘     └─────────────────┘     └─────────────────┘

🤝 Contributing

Contributions are welcome! Help us document the evolving GenAI landscape:

1. Fork the repository
2. Create a new branch
3. Add your LLM/GenAI case study using the established format
4. Submit a pull request

See CONTRIBUTING.md for detailed guidelines.

📄 License

This repository is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgements

• Thanks to all the companies and engineers who shared their LLM/GenAI implementation experiences
• All original sources are linked in each case study

⭐ Star History

⭐ Found this valuable for your GenAI/LLM work? Star the repository to help others discover it! ⭐