How LLMs Work: Top Executive-Level Questions (sloanreview.mit.edu)

Business leaders are increasingly engaging with the intricacies of large language models (LLMs) as they integrate AI tools into their organizations. Understanding how LLMs function is crucial for making informed decisions about their use in enterprise settings. A recent discussion from MIT Sloan School of Management covers ten essential questions about LLMs, addressing common misconceptions regarding their operational mechanics, such as the decision-making process for generating responses, handling corrections, and managing context from previous interactions. This knowledge acts as a foundation for executives to have meaningful conversations with technology partners and enhances their strategic approach to implementing AI. The significance of this dialogue lies in its potential to demystify technical aspects of LLMs, such as token generation, the implications of memory features, and the challenges of hallucinations—misleading or incorrect outputs generated by models. By elucidating techniques like retrieval-augmented generation (RAG) and emphasizing the importance of prompt engineering, the framework promotes improved accuracy and efficiency in AI applications. As firms navigate the complexities of AI technologies, a solid grasp of these foundational concepts equips leaders to optimize workflows while minimizing risks and maximizing the practical benefits of LLM advancements.