Generative AI Architecture Patterns #

Welcome to the comprehensive documentation of architecture patterns for Generative AI systems. This resource aims to provide developers, architects, and technical leaders with practical patterns and best practices for implementing Generative AI solutions.

What are Generative AI Architecture Patterns? #

Architecture patterns in the context of Generative AI are reusable solutions to common problems encountered when designing and implementing AI-powered systems. These patterns capture proven practices, design considerations, and implementation approaches that can be adapted to various use cases across industries.

How to Use This Guide #

This documentation is organized into several categories of patterns:

1. Foundation Models: Patterns for selecting, fine-tuning, and deploying large language models and other foundation models
2. Integration Patterns: Ways to connect AI capabilities with existing systems and workflows
3. Prompt Engineering: Patterns for designing robust prompting systems
4. Retrieval Augmented Generation (RAG): Approaches for enhancing AI outputs with retrieval from knowledge bases
5. Orchestration Patterns: Ways to coordinate multiple AI and traditional components
6. Evaluation and Feedback: Patterns for testing, monitoring, and improving AI systems
7. Responsible AI: Patterns for implementing safety, security, and ethical considerations

Browse through the sections in the sidebar to explore the patterns that best fit your needs.

Contributing #

This is a living document. We welcome contributions from the community. Please see our GitHub repository for information on how to contribute.