In this episode, we unravel the art and science of prompt engineering—the subtle, powerful craft behind guiding large language models (LLMs) to produce meaningful, accurate, and contextually aware outputs. Drawing from the detailed guide by Lee Boonstra and her team at Google, we explore the foundational concepts of prompting, from zero-shot and few-shot techniques to advanced strategies like Chain of Thought (CoT), ReAct, and Tree of Thoughts.

We also dive into real-world applications like code generation, debugging, and translation, and explore how multimodal inputs and model configurations (temperature, top-K, top-P) affect output quality. Wrapping up with a deep dive into best practices—such as prompt documentation, structured output formats like JSON, and collaborative experimentation—you’ll leave this episode equipped to write prompts that actually work. Whether you’re an LLM pro or just starting out, this one’s packed with tips, examples, and aha moments.