# Guidelines to Prompt Large Language Models for Code Generation: An Empirical Characterization

arXiv: https://arxiv.org/abs/2601.13118 · HTML: https://mlsanity.com/paper/2601.13118/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jan 2026.

## Built on by (most cited first, 3 of 3)

- [Prompt Coach: An Empirical Evaluation of an Agentic Tutor for Learning Prompt Engineering in Software Development](https://mlsanity.com/paper/2607.06074/) (2026)
- [Automatically Evolving Prompt Guidelines for Task-Specific Optimization](https://mlsanity.com/paper/2607.14105/) (2026)
- [Self-Evolving Coding Rules for AI Coding Agents](https://mlsanity.com/paper/2610.00650/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) are nowadays extensively used for various types of software engineering tasks, primarily code generation. Previous research has shown how suitable prompt engineering could help developers in improving their code generation prompts. However, so far, there do not exist specific guidelines driving developers towards writing suitable prompts for code generation. In this work, we derive and evaluate development-specific prompt optimization guidelines. First, we use an iterative, test-driven approach to automatically refine code generation prompts, and we analyze the outcome of this process to identify prompt improvement items that lead to test passes. We use such elements to elicit 10 guidelines for prompt improvement, related to better specifying I/O, pre-post conditions, providing examples, various types of details, or clarifying ambiguities. We conduct an assessment with 50 practitioners, who report their usage of the elicited prompt improvement patterns, as well as their perceived usefulness, which does not always correspond to the actual usage before knowing our guidelines. Our results lead to implications not only for practitioners and educators, but also for those aimed at creating better LLM-aided software development tools.