In this second instalment of our two-part series on prompt engineering, we take things to the next level. If you have ever wondered how to get large language models to think more deeply, reason more clearly, or act more independently, this episode is for you. We explore advanced prompting techniques that unlock far more than simple question-and-answer interactions—techniques that help AI reason, plan, and execute in more intelligent ways.

For more technical users—those building with AI, developing workflows, or exploring the limits of what generative AI can do—we will be covering Chain of Thought prompting, which helps guide models through complex reasoning steps, and Tree of Thought prompting, which enables the model to explore multiple problem-solving paths in parallel. We will also briefly touch on ReAct prompting, which allows models to not just reason but take action using tools and external resources.

Even if you’re not deeply technical, this episode still has plenty for you. We cover Automatic Prompt Engineering, a technique where AI helps refine its own instructions for better performance, and explore how to write powerful prompts for code generation, translation, explanation, and debugging—even if you're not a developer.

While some of the strategies we discuss go beyond what most non-technical users will use day to day, they offer a deeper look into what is possible when AI is used with precision and purpose. And to wrap it all up, we share a comprehensive set of best practices in prompt engineering that will help you write clearer, more effective prompts regardless of your background or use case.