High-Performance Microservice (Hexagonal Architecture)
How to Use This Prompt
This AI prompt is designed for gpt-3.5-turbo and can be used to generate high-quality results. It's compatible with multiple AI platforms including gpt-3.5-turbo, gpt-4, gpt-4-turbo, gpt-4o, gpt-4o-mini, claude-3-opus, claude-3-sonnet, claude-3.5-sonnet, claude-3-haiku, gemini-pro, gemini-ultra, gemini-1.5-pro, gemini-1.5-flash, llama-2, llama-3, llama-3.1, mistral-7b, mistral-large, mistral-8x7b, stable-diffusion-2.1, stable-diffusion-xl, stable-diffusion-3, dalle-2, dalle-3, midjourney-v4, midjourney-v5, midjourney-v6, grok, grok-2, leonardo-ai, runway-ml, pika-labs, suno-ai, udio, elevenlabs, perplexity, cohere, nano-banana-pro.
Step-by-Step Instructions
- Copy the prompt content from the Content tab below
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- Paste the prompt into the chat or input field
- Customize any variables or parameters as needed for your specific use case
- Submit the prompt and review the generated results
- Iterate and refine based on the output quality
Common Use Cases
hexagonal architecture
This prompt excels in hexagonal architecture scenarios, providing detailed and contextually relevant outputs for your specific needs.
domain driven design
This prompt excels in domain driven design scenarios, providing detailed and contextually relevant outputs for your specific needs.
microservices
This prompt excels in microservices scenarios, providing detailed and contextually relevant outputs for your specific needs.
golang
This prompt excels in golang scenarios, providing detailed and contextually relevant outputs for your specific needs.
Best Practices
- ✓Be Specific: Add context-specific details to get more targeted results. The more context you provide, the better the AI can understand your requirements.
- ✓Iterate: Don't expect perfect results on the first try. Refine the prompt based on initial outputs to achieve your desired outcome.
- ✓Combine with Other Prompts: This prompt works well when combined with other specialized prompts for complex tasks.
- ✓Review and Edit: Always review AI-generated content and make necessary edits to ensure accuracy and relevance to your specific use case.
What to Expect
When using this prompt, you can expect professional-grade outputs. This prompt has been viewed 7 times and has been tested by our community to ensure effectiveness. The prompt is designed to work seamlessly with gpt-3.5-turbo and provides consistent, reliable results when used correctly.
Prompt Content
{
"system_role": "Backend Infrastructure Lead",
"objective": "Design a Decoupled Microservice using Hexagonal Architecture",
"context": "An 'Order Processing Service' that handles high-throughput e-commerce transactions.",
"design_patterns": {
"architecture": "Ports and Adapters (Hexagonal)",
"domain_logic": "Domain-Driven Design (DDD) - Rich Domain Models",
"communication": "Asynchronous Event-Driven (RabbitMQ/Kafka) for status updates"
},
"implementation_rules": {
"dependency_rule": "Dependencies must point INWARD. Domain logic depends on nothing.",
"ports": "Define Interfaces/Repositories for Persistence and Notification",
"adapters": "Implement adapters for PostgreSQL (Infrastructure) and REST API (Driving)",
"testing": "Focus on Unit Tests for Domain Logic without mocking database"
},
"task_breakdown": [
"Define the Core Domain Entities (Order, OrderItem) and Value Objects",
"Create the 'OrderRepository' Interface (Port)",
"Implement the Service Layer (Use Cases) for 'CreateOrder'",
"Show the Folder Structure adhering to Hexagonal principles"
],
"preferred_language": "Go (Golang) or Node.js (TypeScript) - [User Choice]"
}Details
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