RAG Hallucinates — I Built a Self-Healing Layer That Fixes It in Real Time

Retrieval-Augmented Generation (RAG) systems can fail silently by generating answers that directly contradict the correct source documents they retrieve. These hallucinations are particularly dangerous because the system appears to be working correctly, leading users to trust factually wrong information. Common failures include numeric contradictions, fake citations, flipping the meaning of a sentence, and expressing high confidence in ungrounded responses. To combat this, a self-healing layer can inspect every answer for faithfulness and confidence, detecting specific errors before they reach the user. This lightweight Python solution not only flags these hallucinations in real-time but also includes strategies to automatically fix the incorrect answers.