Reliability

Mitigation strategy

1. **Implement Retrieval-Augmented Generation (RAG):** Integrate the LLM with curated, verified external knowledge bases to ground all outputs in factual data, providing source traceability and substantially reducing knowledge-based hallucinations. This is a foundational necessity for high-stakes, knowledge-intensive deployments. 2. **Establish Continuous Uncertainty Quantification and Hallucination Detection:** Incorporate advanced mechanisms, such as contextual grounding checks, confidence scoring, and auxiliary LLM-as-judge evaluations, to continuously monitor output fidelity. This system must enable selective generation (abstention) and automatically escalate low-confidence or non-factual outputs to human-in-the-loop verification processes. 3. **Enforce Structured Reasoning via Prompt Engineering and Data Quality:** Systematically utilize advanced prompt engineering (e.g., Chain-of-Thought, CoV) to mandate explicit, step-by-step reasoning and logical inference before final output generation. Concurrently, maintain rigorous data governance and automated quality checks on all input, training, and fine-tuning datasets to eliminate foundational errors that increase hallucination risk.