This episode of Beyond the Algorithm explores the unavoidable issue of hallucinations in Large Language Models (LLMs). Using mathematical and logical proofs, the sources argue that the very structure of LLMs makes hallucinations an inherent feature, not just occasional errors. From incomplete training data to the challenges of information retrieval and intent classification, every step in the LLM generation process carries a risk of producing false information. Tune in to understand why hallucinations are a reality we must live with and how professionals can navigate the limitations of these powerful AI tools.