Overview
- New guidance highlights agentic RAG for complex, multi-step tasks, with agents that reformulate queries, adapt retrieval depth, and iterate across sources to strengthen grounding.
- Production lessons stress that most failures start with poor data pipelines, calling for semantic chunking, embedding/version management, and planned re‑embedding to prevent stale results.
- Continuous, multi-level evaluation is urged to measure retrieval precision, answer relevancy, latency, and end‑to‑end business impact across the product lifecycle.
- Operational trade-offs are flagged, including higher token costs and latency from multi-agent flows, alongside security risks such as access‑control gaps and prompt injection that demand governance.
- Practitioners advise reserving agentic designs for cases that truly need complex reasoning, while a new Spring AI tutorial shows how to implement straightforward RAG in Java with PGVector and an Advisor API.