Traditional microservices, REST APIs, and relational databases were designed before AI became a first-class backend concern. This reference guide covers the five core principles of AI-native backend design — prompt-driven APIs, vector vs SQL decisions, event-driven pipelines, AI observability, and where current microservice patterns break.
AI is shifting from code assistant to autonomous agent — and that shift has profound consequences for how Java backend systems are architected. Will REST APIs disappear? Explore the Java perspective on agent orchestration, intent-driven backends, and what CRUD actually becomes in an AI-native world.
A comprehensive production guide to building AI-powered microservices in Java using Spring AI and LangChain4j — covering RAG pipelines, observability, cost optimization, and real-world architecture patterns.
10 copy-paste AI prompts for designing Java microservices. Covers OpenAPI contract design, Circuit Breaker with Resilience4j, Kafka producer and consumer, distributed tracing with Micrometer, Kubernetes health probes, idempotency, event-driven sagas, service-to-service OAuth2 authentication, rate limiting, and full design reviews.
10 copy-paste AI prompts for Hibernate and JPA development. Covers entity relationship design, fixing LazyInitializationException, writing JPQL queries, generating Flyway migrations, second-level cache, bulk operations, soft delete, auditing with @EntityListeners, Criteria API Specifications, and full entity model reviews.
10 copy-paste AI prompts for Spring Boot development. Covers scaffolding REST endpoints, configuring Spring Security JWT, debugging autoconfiguration, global exception handling, production tuning, Actuator health checks, CORS, caching, and migrating from Spring Boot 2.x to 3.x. Works in Claude, ChatGPT, Gemini, and Cursor.
A deep-reference guide to Spring Data JPA covering the full JpaRepository hierarchy, derived query methods, @Query JPQL and native queries, Specifications, pagination, interface and class/record projections, @Transactional rules, and entity auditing with @CreatedDate — with fully annotated production-grade code examples and a decision matrix for choosing the right query strategy.
Complete runnable LLM Gateway project: full directory structure, docker-compose, pom.xml with Spring Boot 3.4.5 and Spring AI 1.1.0, all source files, build instructions, and five end-to-end I/O demos covering normal routing, cache hits, rate limiting, and provider failover.
Complete runnable Spring AI 1.1.0 RAG project: full directory structure, docker-compose, pom.xml, all source files, build instructions, and an end-to-end PDF ingestion and query walkthrough.
Design and implement a production-grade LLM Gateway in Spring Boot. Covers multi-provider routing, circuit breakers, rate limiting, cost tracking, prompt caching, and tenant isolation — the architecture layer every AI-enabled Java application needs.