Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Beyond the Prompt: An Empirical Study of Cursor Rules

Shaokang Jiang, Daye Nam

TL;DR

Large language models require not only explicit prompts but also rich project-wide context to produce high-quality software artifacts. This work presents the first large-scale qualitative study of developer-authored cursor rules across 401 open-source repositories, distilling a five-category taxonomy (Project, Convention, Guideline, LLM Directive, Example) and examining variations by language and domain. The study reveals widespread reuse and duplication of context, a meaningful but uneven adoption of LLM-specific directives, and clear patterns in how context evolves over time and across domains. The findings inform the design of next-generation context-aware AI developer tools and highlight the need for better context transparency and evaluation of context usefulness in practice.

Abstract

While Large Language Models (LLMs) have demonstrated remarkable capabilities, research shows that their effectiveness depends not only on explicit prompts but also on the broader context provided. This requirement is especially pronounced in software engineering, where the goals, architecture, and collaborative conventions of an existing project play critical roles in response quality. To support this, many AI coding assistants have introduced ways for developers to author persistent, machine-readable directives that encode a project's unique constraints. Although this practice is growing, the content of these directives remains unstudied. This paper presents a large-scale empirical study to characterize this emerging form of developer-provided context. Through a qualitative analysis of 401 open-source repositories containing cursor rules, we developed a comprehensive taxonomy of project context that developers consider essential, organized into five high-level themes: Conventions, Guidelines, Project Information, LLM Directives, and Examples. Our study also explores how this context varies across different project types and programming languages, offering implications for the next generation of context-aware AI developer tools.

Beyond the Prompt: An Empirical Study of Cursor Rules

TL;DR

Large language models require not only explicit prompts but also rich project-wide context to produce high-quality software artifacts. This work presents the first large-scale qualitative study of developer-authored cursor rules across 401 open-source repositories, distilling a five-category taxonomy (Project, Convention, Guideline, LLM Directive, Example) and examining variations by language and domain. The study reveals widespread reuse and duplication of context, a meaningful but uneven adoption of LLM-specific directives, and clear patterns in how context evolves over time and across domains. The findings inform the design of next-generation context-aware AI developer tools and highlight the need for better context transparency and evaluation of context usefulness in practice.

Abstract

While Large Language Models (LLMs) have demonstrated remarkable capabilities, research shows that their effectiveness depends not only on explicit prompts but also on the broader context provided. This requirement is especially pronounced in software engineering, where the goals, architecture, and collaborative conventions of an existing project play critical roles in response quality. To support this, many AI coding assistants have introduced ways for developers to author persistent, machine-readable directives that encode a project's unique constraints. Although this practice is growing, the content of these directives remains unstudied. This paper presents a large-scale empirical study to characterize this emerging form of developer-provided context. Through a qualitative analysis of 401 open-source repositories containing cursor rules, we developed a comprehensive taxonomy of project context that developers consider essential, organized into five high-level themes: Conventions, Guidelines, Project Information, LLM Directives, and Examples. Our study also explores how this context varies across different project types and programming languages, offering implications for the next generation of context-aware AI developer tools.

Paper Structure

This paper contains 25 sections, 9 figures, 1 table.

Table of Contents

  1. Introduction
  2. Backgrounds and Related Work
  3. Context Engineering
  4. Rules for AI Coding Assistants
  5. Documentation and Communication in Software Engineering
  6. Methodology
  7. Data Collection and Preprocessing
  8. Dataset
  9. Qualitative Analysis
  10. Threats to Validity
  11. Context Types in Cursor Rules
  12. Project
  13. Convention
  14. Guideline
  15. LLM Directive
  16. ...and 10 more sections

Figures (9)

  • Figure 1: Example cursor rule illustrating project conventions and usage constraints cursor2025rules
  • Figure 2: Overview of the qualitative coding process for developing the taxonomy and coding rules for quantitative analysis.
  • Figure 3: Distribution of the average number of codes for each context type by programming language.
  • Figure 4: Distribution of the average number of codes for each context type by application domain.
  • Figure 5: Distribution of the average number of duplicated lines for each context type by programming language.
  • ...and 4 more figures