Meta has proposed a solution to the issue of hallucinations in large language models (LLMs) through the use of memory layers. This approach enhances model reliability without requiring extensive computational resources. Memory layers improve resource efficiency, accuracy, and scalability, potentially reducing incorrect outputs that can affect critical fields like healthcare, finance, and education. As research progresses, integrating memory layers may lead to more robust AI applications, enhancing user trust and encouraging wider adoption of AI technologies. This innovation represents a significant advancement in addressing the challenges associated with hallucinations in LLMs, paving the way for more reliable artificial intelligence systems.