Reimagining Reality

AI hallucination—the production of fluent but incorrect or fabricated outputs—has become a major challenge for large language models and generative AI. This chapter reviews the evolution of hallucination management, from early recognition to modern mitigation and creative reinterpretation. It defines hallucination in AI contexts and identifies key causes such as data noise, model drift, prompt ambiguity, and domain mismatch. The chapter then examines evaluation frameworks, taxonomies, and metrics, including factual consistency measures, FActScore, and domain-specific benchmarks. Core mitigation strategies are discussed, notably retrieval-augmented generation, multi-stage verification, human-in-the-loop approaches, and prompt optimization. Emerging trends include agentic AI systems with multi-agent validation, sector-specific solutions in healthcare and finance, and viewing hallucination as a driver of human–machine co-creation. The chapter concludes that effective hallucination management is a technical and ethical necessity for trustworthy AI.