In this episode, Lucas and Luna tackle one of the most frustrating problems in modern AI: hallucination in large language models. They break down the specific mechanisms that cause models to confidently generate false information, using the example of a customer support chatbot that invented a refund policy. Lucas explains how retrieval-augmented generation (RAG) and grounding techniques can reduce hallucination rates from over 20 percent to under 5 percent, citing a 2025 paper from Google DeepMind. They also discuss trade-offs with latency and cost, and why no approach is perfect yet. The conversation stays grounded in real numbers and concrete engineering decisions, giving listeners a clear framework for diagnosing and mitigating hallucinations in their own applications.

#Hallucination #LargeLanguageModels #RAG #RetrievalAugmentedGeneration #AIModels #MachineLearning #NLP #Chatbot #PromptEngineering #Grounding #GoogleDeepMind #AIAccuracy #DataScience #Technology #FexingoBusiness #BusinessPodcast #ModelReliability #AISafety

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