# Prompts in the Wild: A Large Analyzed Collection of Transactional Prompts in Code

arXiv: https://arxiv.org/abs/2608.12905 · HTML: https://mlsanity.com/paper/2608.12905/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 41 references that are in the map.
- First posted on arXiv 13 Aug 2026.

## Builds on (most cited first, 19 of 19)

- [Instruction-Following Evaluation for Large Language Models](https://mlsanity.com/paper/2311.07911/) (2023)
- [DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines](https://mlsanity.com/paper/2310.03714/) (2023)
- [WildChat: 1M ChatGPT Interaction Logs in the Wild](https://mlsanity.com/paper/2405.01470/) (2024)
- [LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset](https://mlsanity.com/paper/2309.11998/) (2023)
- [DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models](https://mlsanity.com/paper/2210.14896/) (2022)
- [Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP](https://mlsanity.com/paper/2212.14024/) (2022)
- [VidProM: A Million-scale Real Prompt-Gallery Dataset for Text-to-Video Diffusion Models](https://mlsanity.com/paper/2403.06098/) (2024)
- [The Prompt Report: A Systematic Survey of Prompt Engineering Techniques](https://mlsanity.com/paper/2406.06608/) (2024)
- [When "A Helpful Assistant" Is Not Really Helpful: Personas in System Prompts Do Not Improve Performances of Large Language Models](https://mlsanity.com/paper/2311.10054/) (2023)
- [From Prompts to Templates: A Systematic Prompt Template Analysis for Real-world LLMapps](https://mlsanity.com/paper/2504.02052/) (2025)
- [Task Facet Learning: A Structured Approach to Prompt Optimization](https://mlsanity.com/paper/2406.10504/) (2024)
- [Prompting in the Wild: An Empirical Study of Prompt Evolution in Software Repositories](https://mlsanity.com/paper/2412.17298/) (2024)
- [Task Matters: Knowledge Requirements Shape LLM Responses to Context-Memory Conflict](https://mlsanity.com/paper/2506.06485/) (2025)
- [Promptomatix: An Automatic Prompt Optimization Framework for Large Language Models](https://mlsanity.com/paper/2507.14241/) (2025)
- [WildIFEval: Instruction Following in the Wild](https://mlsanity.com/paper/2503.06573/) (2025)
- [Which Prompting Technique Should I Use? An Empirical Investigation of Prompting Techniques for Software Engineering Tasks](https://mlsanity.com/paper/2506.05614/) (2025)
- [PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines](https://mlsanity.com/paper/2504.14738/) (2025)
- [RepoDebug: Repository-Level Multi-Task and Multi-Language Debugging Evaluation of Large Language Models](https://mlsanity.com/paper/2509.04078/) (2025)
- [Prompts as Software Engineering Artifacts: A Research Agenda and Preliminary Findings](https://mlsanity.com/paper/2509.17548/) (2025)

## Abstract (arXiv)

The behavior of contemporary generative Large Language Models (LLMs) is directly shaped by prompts, unstructured texts that describe the desired output and model behavior. In this paper we argue that prompts are linguistic objects that merit investigation in their own right. To this end, we collect 57.5K unique samples of prompts from GitHub. Specifically, we focus on transactional prompts: reproducible natural language instructions that are integrated into software. To enable the empirical, quantitative study of prompts, we introduce a structured ontology, capturing the properties of prompts as well as their formal and semantic components. Based on this ontology, we transform prompts from unstructured raw texts into richly structured linguistic objects. Analysis of these structured data reveals significant diversity of usage patterns across languages, domains, tasks, and modalities, in a typical Zipf-like distribution where some clearly prevail and others, more diverse, appear in the long tail. To validate the reliability of the ontology-based annotation of the prompts, we perform a comprehensive error analysis across all fields, providing a detailed assessment of annotation quality. We release the dataset together with a browsing and exploration interface (https://github.com/OnlpLab/transactionalPromptsCollection ).