This chapter introduces the fundamentals of communicating with Large Language Models (LLMs). We begin by reviewing what LLMs are and their core capabilities relevant to building applications. You'll learn the definition of prompt engineering and why it is essential for directing LLM behavior.

We will break down the structure of effective prompts, identifying key components like instructions, context, and input data. You'll be introduced to basic prompting techniques for tasks such as text generation and summarization. We will also examine how generation parameters, such as $temperature$ and $top_p$, influence the model's output. The chapter includes hands-on exercises to experiment with creating simple prompts and observing the resulting LLM responses.