Understanding Auto-Code Generation Techniques
In this chapter, we will look at the following key topics:
- What is a prompt?
- Single-line prompts for auto-code generation
- Multi-line prompts for auto-code generation
- Chain-of-thought prompts for auto-code generation
- Chat with code assistant for auto-code generation
- Common building methods of auto-code generation
With the growth in large language model (LLM) applications, one of the interesting use cases, auto-code generation based on user comments, has become popular. The last few years have given rise to multiple code assistants for developers, such as GitHub Copilot, Codex, Pythia, and Amazon Q Developer, among many others. These code assistants can be used to get code recommendations and, in many cases, generate error-free code from scratch, just by passing a few plain text comments that describe what the user requires from the code.
Many of these code assistants are now backed by LLMs. LLMs are pretrained on large publicly available datasets, including public code bases. This training on large corpora of data helps code assistants generate more accurate, relevant code recommendations. To improve the developer’s code-writing experience, these code assistants can not only easily integrate with different integrated development environments (IDE) and code editors but are also readily available with different services offered by most of the cloud providers, with minimal configuration.
Overall, auto-code generation is a process in which the developer has the ability to interact with different code assistants from any supported code editor, using simple plain text comments, to get code recommendations in real time for different supported coding languages.
Before we go deeper into auto-code generation with the help of code assistants later in this chapter, let’s look at the key concept of prompts related to generative AI.
