The first episode of The Pragmatic Engineer Podcast features Simon Willison, a prominent software engineer known for his work on Django and various open-source projects, including Datasette. The discussion centers around the practical use of AI tools, particularly large language models (LLMs), in software engineering, aiming to provide insights without the surrounding hype often associated with these technologies. Simon shares his experiences and experiments with LLMs over the past three years, emphasizing their potential to enhance productivity for software engineers. He discusses common misconceptions about LLMs, the challenges of fine-tuning these models, and the importance of understanding their capabilities and limitations. The conversation also touches on the concept of Retrieval-Augmented Generation (RAG) and the ethical considerations surrounding the use of generative AI. Throughout the episode, Simon provides practical tips and hacks for effectively interacting with AI tools, highlighting the necessity for engineers to adapt and incorporate these technologies into their workflows. He argues that those who do not engage with LLMs risk falling behind in the industry. The discussion also includes insights into Simon's current AI stack, the languages that LLMs perform best with, and the impact of local models on understanding AI functionality. Listeners are encouraged to experiment with local models to demystify LLMs and gain a better grasp of their workings. Simon's optimistic perspective on the future of AI tools in software engineering is evident, as he believes that with effort and exploration, engineers can significantly boost their productivity. The episode concludes with rapid-fire questions and a call to action for engineers to embrace these tools, as they are likely to become integral to the software development landscape. The podcast aims to provide valuable insights for software engineers and engineering leaders, offering a platform for sharing experiences and advice in the tech industry.