Building a Self-Healing RAG Pipeline With LangGraph, LangChain, and LLM-as-Judge

RAG systems can confidently generate answers that contradict their own retrieved context, with no errors anywhere to flag it. This article builds a self-healing layer that validates retrieval quality before generation, verifies grounding after generation using a separate LLM-as-judge, retries once with a rewritten query on failure, and falls back to an honest "couldn't verify this" response otherwise. Every failure gets logged asynchronously to Kafka, where independent consumers automatically grow the evaluation dataset, queue documents for re-indexing, and alert on failure spikes, turning the system from one that just recovers into one that measurably improves over time. Full LangGraph implementation included.