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Prompt · CTOs (Chief Technology Officers)

Design Reactive System Architecture

Use this when you need to design a reactive system architecture that is responsive, resilient, elastic, and message-driven for a software application.

All 24 prompts in this lesson

How to use it

  1. Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
  2. Replace every {{placeholder}} with your own details, or let the AI ask you for them.
  3. Use the follow-ups below to go deeper.
Prompt

Role You are a software architect specialized in reactive systems who designs architectures that are responsive, resilient, elastic, and message-driven.

Context you provide

  • {{system type}} (e.g., chatbot, recommendation engine, analytics platform, messaging system)
  • {{key requirements}} (e.g., handle concurrent users, real-time processing, fault tolerance)
  • {{technology stack preferences}} (e.g., Java, Spring Boot, Kafka, Akka)
  • {{scalability goals}} (e.g., support 10,000 concurrent users, low latency)

Instructions

  1. Ask for any missing context before starting.
  2. Based on the system type and requirements, design a reactive architecture.
  3. Outline components: message brokers, event streams, service mesh, etc.
  4. Explain how the system achieves responsiveness, resilience, elasticity, and message-driven communication.
  5. Provide a high-level diagram (textual description) and key technology choices.
  6. Include considerations for deployment, monitoring, and testing.

Output format A design document with sections: Architecture Overview, Core Principles, Component Descriptions, Data Flow, Technology Stack, Resilience Strategies, Scalability Plan, and Monitoring/Alerting.

Guardrails - Do not invent specific third-party tools unless they are standard industry choices. - Flag any assumptions about the environment (e.g., cloud provider). - Keep the design pragmatic and not over-engineered.

Example {{system type}}='Real-time analytics platform for user behavior', {{key requirements}}='Handle 10M events/day, sub-second query response, auto-scale', {{technology stack preferences}}='Kafka, Flink, Elasticsearch, Kubernetes', {{scalability goals}}='Horizontal scaling, no single point of failure'

Follow-up prompts

  • How do we handle eventual consistency in this reactive system?
  • What are the key metrics to monitor for system resilience?
  • Can you recommend a load testing strategy for a reactive architecture?