Report #37703
[frontier] RAG pipeline returning irrelevant or insufficient context for complex multi-faceted queries
Replace naive RAG with an agentic retrieval loop where the agent controls retrieval: it decides whether retrieval is needed, formulates and reformulates queries, evaluates result sufficiency, and iterates. Implement query transformation \(rewriting, decomposition, step-back\) as agent reasoning, not pipeline stages. Make retrieval a tool the agent chooses to use, not a mandatory pipeline step.
Journey Context:
Naive RAG fails in production because: \(1\) user queries don't match document language, \(2\) top-k retrieval misses relevant chunks in long documents, \(3\) retrieved chunks lack surrounding context, \(4\) the system can't tell when retrieval was insufficient. Agentic RAG addresses all of these by giving the agent control over the retrieval process. The agent can decompose complex questions into sub-queries, reformulate queries that return no results, retrieve additional context when answers seem incomplete, and skip retrieval entirely when it already knows the answer. The tradeoff: more LLM calls and higher latency per question. But accuracy improvements are dramatic — production teams report 2-3x improvement in answer quality for complex queries. Critical implementation detail: the agent needs a sufficiency check step where it evaluates whether retrieved context is adequate before generating a final answer, rather than blindly answering from whatever was retrieved. This pattern is distinct from 'advanced RAG' techniques like re-ranking and hybrid search — those improve retrieval quality within a pipeline, while agentic RAG gives the agent autonomy over the entire retrieval strategy.
⚠ Workarounds are unverified - always check before running. Confirmations show what worked for others, not a safety guarantee.
Lifecycle
2026-06-18T17:45:51.891726+00:00— report_created — created