2 Prompt engineering with ChatGPT

This chapter will teach you the art of prompt engineering, allowing you to harness the full potential of LLMs. We will delve into practical examples and real-world applications of various prompt engineering techniques, and I encourage you to follow alongside the presented content. This hands-on approach ensures that you not only learn the theoretical aspects of prompt design but also its practical application. By the end of this chapter, you will have a strong enough understanding of prompt engineering to tailor ChatGPT’s responses to your specific tasks and use cases.

2.1 Structuring a good prompt

In the previous chapter, we briefly looked at a simple example highlighting the difference between a good prompt and a bad one. These four essential features define an effective prompt: clarity, specificity, instruction, and assumptions.

2.1.1 Clarity

2.1.2 Specificity

2.1.3 Instruction

2.1.4 Assumptions

2.1.5 Recap

2.2 Zero-Shot, One-Shot, and Few-Shot prompts

2.2.1 Zero-Shot prompts

2.2.2 One-Shot prompts

2.2.3 Few-Shot prompts

2.2.4 Reasons to use Few-Shot prompts

2.3 Introduction to role prompting

2.4 Custom instructions for better responses

2.4.1 Information for personalized responses

2.4.2 Preferred response style

2.5 Summary