Large Language Models (LLMs) have revolutionized areas such as customer support and legal analysis but face a critical issue with generating "hallucinations," where outputs appear fluent yet are incorrect or misleading. These problems often stem from context errors, such as missing, ambiguous, or truncated inputs, which lead models to fill gaps with plausible-sounding but inaccurate information. This poses significant risks, particularly in high-stakes domains like healthcare and finance, where erroneous data could have severe consequences. The article emphasizes that addressing these hallucinations involves better contextual management rather than merely improving model architecture. Solutions include providing clear, structured, and relevant input, utilizing retrieval systems to ground responses in factual data, and involving human oversight for critical tasks. By prioritizing context as a vital component, LLMs can transition from impressive prototypes to reliable tools.