Hallucinations will persist whenever LLMs operate in ambiguous or unfamiliar territory, unless there is a fundamental architectural shift away from black box statistical models. There are essentially two options for high-risk use cases given the current state of LLM evolution: Adopt a hybrid solution: hallucination-free, explainable symbolic AI for high-risk use cases, LLMs for everything else. Leave out high-risk use cases, as suggested in #2 above, but that leaves the benefits of the AI unrealized for those use cases. However, the benefits of AI can still be applied to the rest of the organization. The following rank-ordered list is the steps you could take to limit hallucination. 1) Apply hallucination-free, explainable, symbolic AI to high-risk use cases. This is the only foolproof way to eliminate the risk of hallucination in your high-risk use cases. 2) Limit LLM usage to low-risk arenas. Not exposing your high-risk use cases to LLMs is also foolproof but does not bring the benefits of AI to those use cases. Use-case gating is non-negotiable. 3) Mandatory ‘Human-in-the-Loop’ for critical decisions. Reinforcement Learning from Human Feedback (RLHF) is a start, but enterprise deployments need qualified professionals embedded in both model training and real-time decision checkpoints. 4) Governance. Integrate AI safety into corporate governance at the outset. Set clear accountability and thresholds. ‘Red team’ the system. Make hallucination rates part of your board-level risk profile. Follow frameworks like NIST’s AI RMF or the FDA’s new AI guidance 5) Curated, Domain-Specific Data Pipelines. Don’t train models on the internet. Train them on expertly vetted, up-to-date, domain-specific corpora 6) Retrieval-Augmented Architectures (not a comprehensive solution). Combine them with knowledge graphs and retrieval engines. Hybrid models are the only way to make hallucinations structurally impossible, not just unlikely.