Building on the fundamental principles of interacting with Large Language Models, this chapter introduces more refined strategies for prompt construction. These techniques are designed to improve the quality, specificity, and reasoning capabilities of LLM responses, particularly for more complex tasks.

You will learn practical methods including:

• Zero-Shot and Few-Shot Prompting: Understanding when and how to provide examples (or none) within the prompt to guide the model ($k$-shot learning).
• Instruction Following and Role Prompting: Crafting clear directives and assigning specific personas to enhance task adherence.
• Structured Output Generation: Techniques to coax the LLM into generating predictable formats like JSON or Markdown.
• Chain-of-Thought (CoT) Prompting: Encouraging the model to articulate its reasoning process step-by-step, often improving performance on problems requiring logic or calculation.
• Self-Consistency: A method to improve result reliability by sampling multiple reasoning paths.

Mastering these strategies provides finer control over LLM behavior, enabling the development of more sophisticated and reliable applications.