AI Engineering · Phase 0
Refresh the modern Java and Spring Boot foundations every AI integration builds on: records, virtual threads, reactive streams, and containers.
The Java 17-to-21 features that matter most for AI work: records, sealed classes, pattern matching, text blocks and virtual threads — each shown with a concrete AI use case.
Read tutorialFunctional Java refreshed for AI work: streams for document pipelines, Optional for safe metadata access, and CompletableFuture for concurrent model calls — with practical examples.
Read tutorialProject Reactor for AI developers: Mono, Flux, back-pressure and WebFlux — and the one place they are genuinely the right tool, streaming LLM tokens to a browser.
Read tutorialMicroservices patterns that matter for AI systems: API gateway, circuit breakers around model calls, the saga pattern for agent workflows, and where an AI service fits in the topology.
Read tutorialContainerize and deploy a Spring Boot AI application: a production Dockerfile with layered JARs, Kubernetes deployment with secrets for API keys, health probes and resource limits.
Read tutorialMaven and Gradle for Java AI projects: managing Spring AI and LangChain4j versions with BOMs, multi-module layout for projects with separate ingestion and serving, and dependency hygiene.
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