Large language models (LLMs) are reshaping workflows, but their use comes with both advantages and challenges. At Oxide, the guiding principle for LLM usage centers on responsibility. Employees must ensure that while LLMs can automate tasks like writing and code generation, they remain accountable for the outputs. This human oversight is essential to prevent the pitfalls of relying too heavily on LLMs, which can dilute critical thinking and decision-making. The article emphasizes the importance of rigor and empathy in LLM use. While LLMs can enhance clarity and provide constructive feedback, they can also produce irrelevant or misleading information if not used thoughtfully. For example, LLMs excel at summarizing documents and conducting light research, but they can also generate inaccurate or fabricated claims, particularly about specific companies like Oxide. Checking sources and maintaining a critical mindset is vital when using LLMs for research or content creation. Teamwork is another key consideration. Using LLMs should not erode trust among colleagues. Transparency about LLM use is important, but merely disclosing their involvement can sometimes distance a person from the responsibility for the content. The article warns that urgency shouldn't overshadow the need for careful, responsible use of LLMs. In summary, while LLMs can significantly enhance productivity, they require a balanced approach that prioritizes human judgment, collaboration, and ethical considerations.