Spring Data JPA eliminates the EntityManager boilerplate that used to fill every data-access class — you describe the query in a method name or a @Query annotation, Spring generates the implementation at startup. In practice it is both simpler than writing raw JDBC and more dangerous than it looks, because the abstractions hide decisions that have real performance consequences. I have spent four years working with Spring Data JPA across teams of varying sizes — from small startups running H2 in development to larger orgs running Hibernate 6.4 against PostgreSQL under sustained load. The N+1 queries, the LazyInitializationException stack traces, the open-session-in-view debates — I have lived all of them. This guide covers every technique you will encounter in a production Spring Boot application, with honest notes on where each approach breaks down. Tested on Spring Boot 3.4.1, Hibernate 6.4, Spring Data JPA 3.4, Java 21, PostgreSQL 16.

Every Java team building AI features eventually faces the same set of problems: a single LLM provider goes down during a critical demo, a runaway loop burns $500 of API credit overnight, or two product teams independently hard-code OpenAI keys into separate microservices and you lose all cost visibility. The LLM Gateway pattern solves all of these — and it’s the most underbuilt component in the Java AI ecosystem today. This post walks through designing and implementing a production-grade LLM Gateway in Spring Boot: multi-provider routing with automatic failover, circuit breaking, rate limiting, cost tracking, semantic caching, and tenant isolation.

Most RAG tutorials stop at “load a PDF, embed it, ask a question.” That works in a demo. In production — handling 10 000 documents, diverse query types, and user expectations of factual accuracy — it falls apart within weeks. This guide walks through every layer of a production-grade Retrieval-Augmented Generation (RAG) system built entirely in Java using Spring AI 1.1.0: from chunking strategy selection and vector store tuning to reranking, hallucination guards, cost tracking, and observability.

Java 25 shipped as an LTS release in September 2025. If you are already on Java 21 LTS, the jump is mostly smooth — most new features were previews in 21 that are now final — but there are a handful of deprecations, build-tool bumps, and behavioural tweaks that will trip you up if you do not prepare. This guide walks through the upgrade end-to-end with a migration checklist and AI prompts to automate the tedious parts.