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A conceptual guide on deciding when to trust LLM outputs in high-stakes professional contexts like legal, clinical, and financial work, emphasizing the need for critical evaluation skills.
This paper presents a neuro-symbolic verification architecture for LLM outputs in high-stakes domains, combining formal symbolic methods with neural semantic analysis. Evaluated on a medical device damage assessment system, it achieves over 83% hallucination detection for structured entities and 30% reduction in report creation time.