AI Engineering · Phase 5
Architectural patterns for shipping AI inside real systems: streaming APIs, event-driven pipelines, semantic caching, multi-tenancy and low-latency serving.
Design robust AI REST APIs in Spring Boot: streaming with Server-Sent Events, async processing for long tasks, timeouts, back-pressure and the API patterns that make AI features reliable.
Read tutorialBuild event-driven AI systems with Kafka and Spring Boot: async AI processing pipelines, decoupling model calls from request threads, dead-letter handling and back-pressure for LLM workloads.
Read tutorialIntegrate AI into CI/CD pipelines: automated code review, test generation, documentation and PR triage — with the precision discipline and guardrails that keep these bots useful, not noisy.
Read tutorialBuild AI-powered search in Java: hybrid keyword-plus-vector search, faceted filtering, query understanding, personalization and re-ranking — beyond both keyword search and naive RAG.
Read tutorialDesign production chatbots in Java: intent classification, dialog state management, slot filling, multi-turn context, tool integration and handoff to humans — beyond a single ChatClient call.
Read tutorialApply LLMs to data engineering in Java: text-to-SQL with safety guards, AI-assisted data cleaning, schema mapping and anomaly detection — where AI helps and where it must be constrained.
Read tutorialObserve LLM applications in production: distributed tracing of model and retrieval calls, LangFuse and OpenTelemetry GenAI conventions, span attributes, and cost dashboards for Java teams.
Read tutorialBuild multi-tenant AI systems in Java: strict tenant isolation in retrieval, per-tenant quotas and rate limits, cost allocation, and data residency — keeping tenants apart safely at scale.
Read tutorialCut LLM cost and latency with caching: exact-match caching, semantic caching by embedding similarity, provider prompt caching, and invalidation — with Redis and Java examples.
Read tutorialServe LLMs with low latency: time to first token, streaming, continuous batching, vLLM and TGI, speculative decoding, and the latency levers available whether you self-host or use an API.
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