Large Language Models (LLMs) and Large Reasoning Models (LRMs) hold transformative potential for high-stakes domains such as finance and law, but their tendency to hallucinate poses a critical reliability risk. This talk explores detection strategies, including uncertainty estimation, reasoning consistency checks, and factual validation, paired with mitigation approaches such as knowledge grounding, prompt engineering, and confidence calibration. What sets this discussion apart is the emphasis on root cause awareness: by categorizing hallucination sources into model, data, and context-related factors, detection and mitigation strategies can be precisely matched to the underlying cause rather than applied as generic fixes.