AI Summary

Most teams building retrieval‑augmented generation (RAG) systems for high‑stakes classification route every ambiguous case directly to a language model, relying on retrieved context to resolve it. While this approach works in demos, it breaks down when an audit, regulator, or compliance officer demands an explanation for a decision made months earlier. The author, who spent the past year developing RAG‑based classification in regulated enterprise environments, notes that a wrong answer carries far more risk than a simple chatbot error. Consequently, such settings require a design philosophy that limits what reaches the LLM, ensuring decisions remain auditable and compliant.

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