Spring AI + RAG: Build Production-Grade AI with Your Data

Spring AI RAG system design covering ingestion, chunking, retrieval, and prompt reliability.

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Design end-to-end RAG systems using Spring AI, following backend system design principles rather than demo-style implementations.
Build repeatable ingestion pipelines for PDFs, wiki documents, and database content with clear structure and metadata.
Implement effective chunking and embedding pipelines that directly impact retrieval quality and correctness.
Design metadata-aware retrieval pipelines and integrate them cleanly into backend chat flows.
Control LLM behavior using explicit prompt orchestration, grounding rules, and source-aware answers.
Manage the full knowledge lifecycle by safely adding, updating, and deleting data without corrupting retrieval results.

Basic experience with Java and Spring Boot (REST APIs, configuration, project structure).
Comfortable working with databases and general backend application concepts.
Familiarity with IDE-based development and running applications locally.
No prior AI, RAG, or Spring AI experience required — all AI concepts are covered from scratch.

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