# Exploring Prompt Engineering Practices in the Enterprise

arXiv: https://arxiv.org/abs/2403.08950 · HTML: https://mlsanity.com/paper/2403.08950/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 30 references that are in the map.
- First posted on arXiv 13 Mar 2024.

## Builds on (most cited first, 18 of 18)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023)
- [PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts](https://mlsanity.com/paper/2202.01279/) (2022)
- [PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization](https://mlsanity.com/paper/2310.16427/) (2023)
- [PromptChainer: Chaining Large Language Model Prompts through Visual Programming](https://mlsanity.com/paper/2203.06566/) (2022)
- [AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap](https://mlsanity.com/paper/2306.01941/) (2023)
- [Guiding Large Language Models via Directional Stimulus Prompting](https://mlsanity.com/paper/2302.11520/) (2023)
- [Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation with Large Language Models](https://mlsanity.com/paper/2208.07852/) (2022)
- [The Foundation Model Transparency Index](https://mlsanity.com/paper/2310.12941/) (2023)
- ["What It Wants Me To Say": Bridging the Abstraction Gap Between End-User Programmers and Code-Generating Large Language Models](https://mlsanity.com/paper/2304.06597/) (2023)
- [Investigating Explainability of Generative AI for Code through Scenario-based Design](https://mlsanity.com/paper/2202.04903/) (2022)
- [How to Prompt? Opportunities and Challenges of Zero- and Few-Shot Learning for Human-AI Interaction in Creative Applications of Generative Models](https://mlsanity.com/paper/2209.01390/) (2022)
- [Understanding Users' Dissatisfaction with ChatGPT Responses: Types, Resolving Tactics, and the Effect of Knowledge Level](https://mlsanity.com/paper/2311.07434/) (2023)
- [PromptAid: Prompt Exploration, Perturbation, Testing and Iteration using Visual Analytics for Large Language Models](https://mlsanity.com/paper/2304.01964/) (2023)
- [User Intent Recognition and Satisfaction with Large Language Models: A User Study with ChatGPT](https://mlsanity.com/paper/2402.02136/) (2024)
- [Prompts have evil twins](https://mlsanity.com/paper/2311.07064/) (2023)

## Built on by (most cited first, 11 of 11)

- [Oolong: Evaluating Long Context Reasoning and Aggregation Capabilities](https://mlsanity.com/paper/2511.02817/) (2025)
- [Prototyping Multimodal GenAI Real-Time Agents with Counterfactual Replays and Hybrid Wizard-of-Oz](https://mlsanity.com/paper/2510.06872/) (2025)
- [Gensors: Authoring Personalized Visual Sensors with Multimodal Foundation Models and Reasoning](https://mlsanity.com/paper/2501.15727/) (2025)
- [CrunchLLM: Multitask LLMs for Structured Business Reasoning and Outcome Prediction](https://mlsanity.com/paper/2509.10698/) (2025)
- [Georeferencing complex relative locality descriptions with large language models](https://mlsanity.com/paper/2512.14228/) (2025)
- [LAPPI: Interactive Optimization with LLM-Assisted Preference-Based Problem Instantiation](https://mlsanity.com/paper/2512.14138/) (2025)
- [Context-Mediated Domain Adaptation in Multi-Agent Sensemaking Systems](https://mlsanity.com/paper/2603.24858/) (2026)
- [Toward Robust Open-set Adaptation: Synapse Consolidation Inspired by Rac1/MAPK Pathways](https://mlsanity.com/paper/2604.00533/) (2026)
- [Alignment has a Fantasia Problem](https://mlsanity.com/paper/2604.21827/) (2026)
- [Less Back-and-Forth: A Comparative Study of Structured Prompting](https://mlsanity.com/paper/2605.20149/) (2026)
- [The Garden of Forking Prompts: How Users Explore Narrative Space in Story Generation](https://mlsanity.com/paper/2609.14677/) (2026)

## Abstract (arXiv)

Interaction with Large Language Models (LLMs) is primarily carried out via prompting. A prompt is a natural language instruction designed to elicit certain behaviour or output from a model. In theory, natural language prompts enable non-experts to interact with and leverage LLMs. However, for complex tasks and tasks with specific requirements, prompt design is not trivial. Creating effective prompts requires skill and knowledge, as well as significant iteration in order to determine model behavior, and guide the model to accomplish a particular goal. We hypothesize that the way in which users iterate on their prompts can provide insight into how they think prompting and models work, as well as the kinds of support needed for more efficient prompt engineering. To better understand prompt engineering practices, we analyzed sessions of prompt editing behavior, categorizing the parts of prompts users iterated on and the types of changes they made. We discuss design implications and future directions based on these prompt engineering practices.