3 Prompt engineering and problem formulation

 

This chapter covers

  • Exploring the essentials of prompt engineering
  • Techniques for effective AI prompting
  • Comparing zero-shot, single-shot, few-shot, and many-shot prompting
  • Advanced strategies for optimizing AI responses
  • The role of problem formulation in AI interactions

In this chapter, we turn our focus to the fundamental skills of prompt engineering and problem formulation, key elements for anyone interacting with AI. We need to find the right words and structure to make our interactions as productive as possible. This skill is less about complex language and more about precision and understanding the AI’s capabilities.

Prompt engineering requires an understanding of which type of prompt best suits the task at hand, much like selecting the best tool for a specific job. As AI models become more intuitive and sophisticated, simpler tasks require less detailed prompting. However, the art of problem formulation, like understanding the blueprint before construction, remains indispensable. Problem formulation is envisioning the end goal and guiding the AI to achieve it effectively.

3.1 Prompt engineering

3.2 The spectrum of AI prompting

3.2.1 Zero-shot prompting

3.2.2 Single-shot prompting

3.2.3 Few-shot prompting

3.2.4 Many-shot prompting

3.2.5 The future of prompt engineering

3.3 The mechanics of a good prompt

3.3.1 Key principles in prompt construction

3.4 Introductory prompts for IT roles

3.4.1 Awesome Prompts Lab

3.5 Advanced techniques

3.6 Best practices and common mistakes

3.6.1 Learning from success

3.6.2 Pitfalls to avoid

3.6.3 Meta-prompts and hints

3.7 Problem formulation

3.7.1 Incorporating problem formulation

3.8 Summary

3.9 Prompts used to write this chapter

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