Prompt Engineering Best Practices for Code Generation Tools

Abstract

Automated code generation has taken a major leap with the emergence of the Large Language Models (LLMs), including the Codex of OpenAI, Google AlphaCode and Code LLaMA of Meta. Nonetheless, in a bid to do justice to the abilities of such models, prompt engineering has become an important methodology. Prompt engineering is the art of carefully framing input queries or commands to achieve maximum quality and reliability of output. The intention of a user and machine interpretation can be met through well-constructed prompts in the process of code generation, resulting in efficient and safe source code with syntactic correctness. In this paper, best practices in the specific case of prompt engineering on code generation tools will be discussed. We talk about the fundamentals of efficient prompts, the method of few-shot learning, the zero-shot learning method, chain-of-thought prompting, and contrastive examples. In addition, we compare timely templates, tokenization optimization options, and role-based system messages. Through controlled experiments on an array of LLM platforms before October 2024, we will present some empirical evidence, pointing out the role of prompt structure in terms of the error rate, code completeness, and code quality one should expect. Prompt variants, evaluation metrics (e.g., functional correctness, cyclomatic complexity), and other pitfalls (e.g. prompt leakage, hallucination) are described in tables and figures in this study. We propose a systematic approach to building, then iterating and testing prompts that can be used to address code generation activities using over a dozen examples, such as the synthesis of algorithms, API call generation, and debugging. Findings indicate that an increase of up to 45 percent improvement in code precision and a 60 percent decrease in runtime bugs occur with the best-practice prompt structures. The results of our investigations proposed guidelines and taxonomy to support researchers in their lofty goals and developers and educators in their ambitions to exploit timely engineering in the construction of production-quality coding