Air Canada Lost a Lawsuit Because Their RAG Hallucinated. Yours Will Too

Popular RAG hallucination detection tools like RAGAS and DeepEval fail to catch 83% of production errors in real-world applications. Cleanlab's benchmarks reveal that most detection methods barely outperform random guessing because they only measure aleatoric uncertainty (known unknowns) rather than epistemic uncertainty (unknown unknowns). TLM (Trustworthy Language Model) achieves significantly better results by combining self-reflection, multi-response consistency checks, and probabilistic measures, reducing human review costs by 4.5x while maintaining quality. The Air Canada lawsuit demonstrates that RAG hallucinations create legal liability, not just technical problems, making comprehensive uncertainty estimation critical for high-stakes production deployments.