In a podcast with Dan Shipper, the discussion centered on prompt engineering, focusing on its foundational elements: the prompt, evaluation, and dataset, which are crucial for creating effective AI applications. The podcast highlighted that while some aspects of prompt engineering can be automated, domain context remains vital and cannot be fully automated, emphasizing the role of non-technical prompt engineers who rely on subject matter expertise rather than technical skills. Effective prompt engineering is likened to the scientific method, prioritizing rapid testing and iteration over theoretical approaches common in academic research. Best practices include modular prompting, starting with simple evaluations, and integrating domain expertise, suggesting that success in AI development will hinge on empowering non-technical users and leveraging domain knowledge rather than solely focusing on machine learning expertise. PromptLayer is mentioned as a popular platform supporting these practices by providing tools for building AI applications with strong domain knowledge.