An Introduction to Prompt Engineering
In this chapter, we will introduce the practice of prompt engineering, a field of study that started in 2020, which offers advanced techniques for refining prompts to achieve more reliable, predictable, and desirable outcomes in large language model (LLM)-based applications.
We will focus on an effective prompt engineering technique for code generation with GenAI: few-shot learning, also known as in-context learning. This approach, when used in the context of code-related tasks, guides the model toward producing outputs that align with a specific coding style or objective.
We will focus on how the few-shot learning technique can enhance the model’s output for more accurate and style-consistent code generation. While there are many different uses of few-shot learning in code-related tasks, the focus on style is a useful example for maintaining consistency in a repository. This includes enforcing type hints, adding docstrings, or...
