5 Prompt engineering techniques

 

This chapter covers

  • Prompt engineering to optimize AI model outputs in web applications
  • Leveraging few-shot learning to enable quick adaptation of AI models to new tasks
  • Using Chain of Thought prompting to improve AI reasoning and problem-solving
  • Using embeddings for semantic search and content recommendations in AI-powered apps.

Our web application aims to provide engaging AI-powered interactions for users. So far, we've focused on the technical foundations, leveraging cutting-edge tools like Next.js, React, and the Vercel AI SDK to seamlessly integrate various AI models. While this has helped improve the overall user experience, we've relied on the same generic prompt message, instructing the AI to be polite and respond to queries. To offer a truly unique solution, we need to go beyond this one-size-fits-all approach. This is what we call Prompt engineering or the art of carefully crafting and refining the prompts and contextual information submitted to the AI model. By fine-tuning the prompts in this chapter, we can enable the AI to perform more efficiently and deliver more accurate, contextual responses that truly meet the needs of our users. We will review important concepts of prompt engineering, experimenting with different prompts and contextual cues to unlock the full potential of the AI models powering our web application.

5.1 Introduction to prompt engineering

5.1.1 What exactly are prompts?

5.1.2 Prompt types

5.1.3 How to manage prompts

5.2 Few-shot learning

5.2.1 Examples of few-shot learning

5.2.2 General methodology for creating few-shot learning prompts

5.3 Chain of thought prompting

5.3.1 Example of Chain of thought prompting

5.3.2 General methodology for creating chain of thought prompts

5.4 Embeddings

5.4.1 Using embeddings with Vercel AI SDK

5.4.2 Use case: IT Support Knowledge Base

5.5 Summary