Article: Beyond RAG: Architecting Context-Aware AI Systems with Spring Boot

The shift from RAG to context-aware architectures reflects a maturity milestone in production AI systems. I've seen teams struggle with generic retrieval pipelines that ignore user permissions, session context, and audit requirements—exactly what CAG addresses. Spring Boot's role here matters because it bridges the operational gap: your data platform already indexes documents, but now you need a context manager that enforces access control, tracks lineage, and personalizes responses without rebuilding your entire stack. The practical implication is significant: you're no longer just optimizing retrieval quality, you're architecting trust and compliance into the prompt itself. This aligns with broader trends toward composable AI infrastructure where context becomes a first-class citizen alongside embeddings and models. My recommendation: audit your current RAG implementations for context leakage—cases where sensitive user data or business logic silently influences outputs. If you find gaps, a context manager layer is worth the engineering investment because it prevents costly compliance failures and makes your AI outputs defensible.