azure-genai-design-patterns
🥈SilverAutomate agentic AI system design using Azure's generative AI design patterns. Operations teams can streamline LLM integration, reducing development time and improving system autonomy. Connects to Azure AI services and Python workflows.
6861220Updated Today
Intermediate30min to implementautomation
Saves ~150 min per use
Quick InstallView Source
git clone https://github.com/microsoft/azure-genai-design-patterns.gitWorks with:
Claude
Overview
About This Skill
https://github.com/microsoft/azure-genai-design-patterns
Use Cases
Automate customer support interactions using LLMs to provide instant responses.
Implement dynamic goal adjustment in AI systems based on real-time data inputs.
Create workflows that seamlessly transition between different applications to enhance productivity.
Develop AI agents that can autonomously manage and optimize business processes.
Setup & Installation
Quick Install
No install command available. Check the GitHub repository for manual installation instructions.
Alternative Install (Git Clone)
git clone https://github.com/microsoft/azure-genai-design-patternsRequirements
- Claude Code or compatible AI agent
- Works with: Claude
Quick Start Guide
1
Install the Skill
Copy the install command above and run it in your terminal.
2
Open Your AI Agent
Launch Claude Code, Cursor, or your preferred AI coding agent.
3
Try It Out
Use the prompt template or examples below to test the skill.
4
Customize
Adapt the skill to your specific use case and workflow.
Usage Examples
Prompt Template
Generate a design pattern for building an Agentic AI system using Azure's GenAI services. The system should be designed for [COMPANY], operating in the [INDUSTRY] sector. Focus on [DATA] processing and integration with existing systems. Include considerations for scalability, security, and compliance.
Example Output
# Design Pattern: Azure GenAI Data Processing Pipeline ## Overview This design pattern outlines a scalable and secure data processing pipeline for [COMPANY], a leading firm in the [INDUSTRY] sector. The pipeline leverages Azure's GenAI services to process and analyze large volumes of [DATA] while ensuring compliance with industry regulations. ## Architecture Components - **Data Ingestion Layer**: Azure Event Hubs for real-time data ingestion - **Processing Layer**: Azure Functions with GenAI integration for data transformation - **Storage Layer**: Azure Blob Storage for raw data and Azure Cosmos DB for processed data - **Orchestration Layer**: Azure Logic Apps for workflow management - **Monitoring Layer**: Azure Monitor for performance tracking and alerting ## Key Considerations - **Scalability**: The pipeline is designed to handle varying data loads with auto-scaling enabled on Azure Functions - **Security**: Data is encrypted in transit and at rest, with role-based access control (RBAC) implemented - **Compliance**: The solution adheres to [INDUSTRY] sector regulations, including data retention policies and audit trails ## Implementation Steps 1. Set up Azure Event Hubs for data ingestion 2. Develop Azure Functions to process [DATA] using GenAI services 3. Configure Azure Blob Storage and Cosmos DB for data storage 4. Create Logic Apps workflows to orchestrate the data processing pipeline 5. Implement Azure Monitor for performance tracking and alerting
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