Microservices architecture introduces a class of problems that monoliths simply do not have: partial failures, distributed transactions, network latency between services, schema versioning across independent deployments, and the operational complexity of coordinating dozens of independently deployed components. The first time I designed a circuit breaker configuration for a production service, I guessed the thresholds. The circuit opened too aggressively during a transient network hiccup and caused more downtime than the downstream failure it was meant to protect against. The second time, I ran the numbers from the downstream service’s SLA and worked backwards. There was no tutorial that taught me to do that — I learned it the hard way. These 10 prompts encode the questions you need to answer before you configure Resilience4j, write an OpenAPI contract, or design a Kafka consumer group — so the AI produces configuration that fits your actual failure scenario rather than a generic example. Getting these patterns right requires both architectural experience and fluency with a large surface area of libraries and specifications. AI assistants are well-positioned here — they know the Resilience4j API, the OpenAPI specification, the Kafka consumer group model, the Micrometer tracing API, and the Kubernetes health probe semantics.

This post gives you 10 copy-paste AI prompts for designing Java microservices. Each prompt targets a specific cross-cutting architecture concern and is engineered to produce production-grade, runnable output rather than pseudocode.

For related reading, see 10 AI Prompts for Spring Boot Development and the Java series. For the Spring AI and LLM Gateway patterns, see the LLM Gateway for Java Microservices post.