Large language models (LLMs) have revolutionized how we interact with AI, but they still face challenges in reliability, safety, and providing truly useful information. Imagine a scenario where instead of relying on a single AI 'expert,' you could consult a panel of specialized AIs, each offering unique insights. This is the core idea behind Multi-expert Prompting, a novel technique aimed at enhancing LLM outputs by simulating a collaborative environment of multiple AI experts. The process begins by prompting the LLM to identify several expert roles relevant to the given instruction. For example, if the question is about the ethical implications of artificial intelligence, the LLM might identify an ethicist, a computer scientist, and a sociologist as relevant experts. The LLM then generates responses to the instruction from the perspective of each identified expert. The real magic happens in the next stage: aggregating these diverse expert opinions into a single, coherent response. Inspired by the Nominal Group Technique, a structured decision-making process, the LLM undertakes a series of subtasks. It identifies points of agreement and conflict between the expert answers, resolves those conflicts based on majority agreement and its own knowledge base, and pulls in unique perspectives offered by individual experts. This process creates a response that is more well-rounded and nuanced than any single expert could provide on their own. Testing this method on various benchmarks like TruthfulQA, FactualityPrompt, and others reveals some exciting results. Multi-expert Prompting significantly improves the truthfulness and factuality of LLM-generated text while reducing toxicity and harmful content. It even achieved state-of-the-art accuracy on some tests, highlighting the potential of this collaborative approach. Furthermore, the research demonstrates that a concise, one-sentence description of each expert’s role is sufficient for the LLM to effectively embody that persona, making the technique efficient and scalable. It also underscores the value of incorporating diverse viewpoints, as Multi-expert Prompting consistently outperformed methods relying on single expert identities. However, challenges remain. The technique relies heavily on the LLM’s existing knowledge base and can sometimes struggle with accurately identifying unique viewpoints. Also, as with any AI system, ensuring fairness and mitigating potential biases amplified by the aggregation process are critical considerations for future development. Multi-expert Prompting represents a significant step towards building more reliable and trustworthy LLMs. By integrating multiple perspectives and carefully resolving conflicts, this technique has the potential to unlock a new level of sophistication in how we utilize LLMs in research, education, and everyday life. It paves the way for AI systems that don't just answer questions but provide comprehensive, well-reasoned, and multi-faceted perspectives on complex issues.