🟢 Introduction

You have made it through the beginning stages of Prompt Engineering! Now you can dive into some intermediate techniques which can really take your prompting to the next level.

Here, you are going to shift your focus from the tasks that GenAI can solve, onto the prompting techniques themselves. According to The Prompt Report¹, "a prompting technique is a blueprint that describes how to structure a prompt, prompts, or dynamic sequencing of multiple prompts. A prompting technique may incorporate conditional or branching logic, parallelism, or other architectural considerations spanning multiple prompt". In the coming lessons, we will focus on more technical aspects of prompting such as prompt structure and design.

This module will expose you to moderately complex, research-based prompt engineering techniques. You'll learn how to implement these techniques to improve the performance of your GenAI applications. Some topics we will explore are Chain of Thought, Self-Consistency, and Generated knowledge. We will also revisit a technique we have already touched on, Role Prompting, and expand on its use. Along the way, you will also learn more about where prompting LLMs (Large Language Models) can fail.

By the end of this module, you will have a fundamental understanding of many of the world's most used prompting techniques¹ and be able to apply them to a myriad of tasks.