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.
A deep-reference guide to Java Records — the concise, immutable data-carrier syntax introduced in Java 16 and matured in Java 21. Covers declaration syntax, compact constructors, custom accessors, equals/hashCode/toString behaviour, records with Jackson, records with Hibernate, records in Spring Boot, and key limitations — with fully annotated code examples and a comparison to plain Java classes.
A complete reference guide to Java Generics: generic classes and methods, bounded type parameters, wildcards, the PECS rule, type erasure, generic interfaces, common pitfalls, and AI prompts to modernise raw-type code.
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.
Build a production-ready RAG pipeline in Java using Spring AI 1.0. Covers vector store setup, smart chunking strategies, reranking, hallucination detection, observability, and anti-patterns to avoid.
A hands-on comparison of Java virtual threads (Project Loom) against platform threads — with a 28× I/O benchmark, CPU-bound proof, memory comparison table, HttpClient real-world example, architecture diagram, and a decision table for when NOT to use virtual threads.
A practical walkthrough of the headline features landing in Java 21 (LTS) through Java 25 (LTS): records, pattern matching, virtual threads, sequenced collections, scoped values, and structured concurrency — with runnable examples and AI prompts you can copy straight into Claude or ChatGPT to migrate your own code.