What You Prompt is What You Get: Increasing Transparency of Prompting Using Prompt Cards

• URL: http://arxiv.org/abs/2603.12741v1
• Date: Fri, 13 Mar 2026 07:38:25 GMT
• Title: What You Prompt is What You Get: Increasing Transparency of Prompting Using Prompt Cards
• Abstract summary: A critical challenge facing the field is the lack of standardised prompt documentation and evaluation practices.<n>We propose the use of prompt cards, structured summaries of prompt engineering practices inspired by the concept of model cards.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid advancement and impressive capabilities of large language models (LLMs) have given rise to the field of prompt engineering, the practice of crafting inputs to guide LLMs toward high-quality, task-relevant outputs. A critical challenge facing the field is the lack of standardised prompt documentation and evaluation practices. Prompts can be long, complex and difficult to evaluate on subjective tasks. To address this challenge, we propose the use of prompt cards, structured summaries of prompt engineering practices inspired by the concept of model cards. Through prompt cards, the specific goals, considerations and steps taken during prompt engineering can be systematically documented and assessed. We present the prompt card approach and illustrate it on a specific task called wordalisation, in which structured numerical data is transformed into text. We argue that a well-structured prompt card can enable better reproducibility, transparency, improve prompt methodology and give an effective alternative to benchmarking for judging the quality of generated texts. By systemically capturing underlying model details, prompt intent, contextualisation strategies, evaluation practices and ethical considerations, prompt cards make explicit the often implicit design decisions that shape system behaviour. Documenting these choices is important as prompting increasingly involves complex pipelines with multiple moving parts.

Related papers

• Improving Alignment Between Human and Machine Codes: An Empirical Assessment of Prompt Engineering for Construct Identification in Psychology [0.0]
  We present an empirical framework for optimizing prompt performance for identifying constructs in texts via prompt engineering.<n>We experimentally evaluate five prompting strategies -codebook-guided empirical prompt selection, automatic prompt engineering, persona prompting, chain-of-thought reasoning, and explanatory prompting.<n>Across three constructs and two models, the classifications most aligned with expert judgments resulted from a few-shot prompt combining codebook-guided empirical prompt selection with automatic prompt engineering.
  arXiv  Detail & Related papers  (2025-12-03T14:07:42Z)
• IPO: Interpretable Prompt Optimization for Vision-Language Models [40.83071220530289]
  This paper introduces a simple but interpretable prompt (IPO) IPO utilizes large language models (LLMs) to generate textual prompts dynamically. We incorporate a large multimodal model (LMM) to condition on visual content by generating image descriptions.
  arXiv  Detail & Related papers  (2024-10-20T14:10:22Z)
• Venn Diagram Prompting : Accelerating Comprehension with Scaffolding Effect [0.0]
  We introduce Venn Diagram (VD) Prompting, an innovative prompting technique which allows Large Language Models (LLMs) to combine and synthesize information across documents. Our proposed technique also aims to eliminate the inherent position bias in the LLMs, enhancing consistency in answers by removing sensitivity to the sequence of input information. In the experiments performed on four public benchmark question-answering datasets, VD prompting continually matches or surpasses the performance of a meticulously crafted instruction prompt.
  arXiv  Detail & Related papers  (2024-06-08T06:27:26Z)
• Efficient Prompting Methods for Large Language Models: A Survey [50.82812214830023]
  Efficient Prompting Methods have attracted a wide range of attention.<n>We discuss Automatic Prompt Engineering for different prompt components and Prompt Compression in continuous and discrete spaces.
  arXiv  Detail & Related papers  (2024-04-01T12:19:08Z)
• Exploring Prompt Engineering Practices in the Enterprise [3.7882262667445734]
  A prompt is a natural language instruction designed to elicit certain behaviour or output from a model. For complex tasks and tasks with specific requirements, prompt design is not trivial. We analyze sessions of prompt editing behavior, categorizing the parts of prompts users iterated on and the types of changes they made.
  arXiv  Detail & Related papers  (2024-03-13T20:32:32Z)
• Intent-based Prompt Calibration: Enhancing prompt optimization with synthetic boundary cases [2.6159111710501506]
  We introduce a new method for automatic prompt engineering, using a calibration process that iteratively refines the prompt to the user intent. We demonstrate the effectiveness of our method with respect to strong proprietary models on real-world tasks such as moderation and generation.
  arXiv  Detail & Related papers  (2024-02-05T15:28:43Z)
• InfoPrompt: Information-Theoretic Soft Prompt Tuning for Natural Language Understanding [51.48361798508375]
  We develop an information-theoretic framework that formulates soft prompt tuning as maximizing mutual information between prompts and other model parameters. We show that InfoPrompt can significantly accelerate the convergence of the prompt tuning and outperform traditional prompt tuning methods.
  arXiv  Detail & Related papers  (2023-06-08T04:31:48Z)
• Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery [55.905769757007185]
  We describe an approach to robustly optimize hard text prompts through efficient gradient-based optimization. Our approach automatically generates hard text-based prompts for both text-to-image and text-to-text applications. In the text-to-text setting, we show that hard prompts can be automatically discovered that are effective in tuning LMs for classification.
  arXiv  Detail & Related papers  (2023-02-07T18:40:18Z)
• TEMPERA: Test-Time Prompting via Reinforcement Learning [57.48657629588436]
  We propose Test-time Prompt Editing using Reinforcement learning (TEMPERA) In contrast to prior prompt generation methods, TEMPERA can efficiently leverage prior knowledge. Our method achieves 5.33x on average improvement in sample efficiency when compared to the traditional fine-tuning methods.
  arXiv  Detail & Related papers  (2022-11-21T22:38:20Z)
• MetaPrompting: Learning to Learn Better Prompts [52.914694884515534]
  We propose a new soft prompting method called MetaPrompting, which adopts the well-recognized model-agnostic meta-learning algorithm. Extensive experiments show MetaPrompting brings significant improvement on four different datasets.
  arXiv  Detail & Related papers  (2022-09-23T09:01:05Z)
• RLPrompt: Optimizing Discrete Text Prompts With Reinforcement Learning [84.75064077323098]
  This paper proposes RLPrompt, an efficient discrete prompt optimization approach with reinforcement learning (RL) RLPrompt is flexibly applicable to different types of LMs, such as masked gibberish (e.g., grammaBERT) and left-to-right models (e.g., GPTs) Experiments on few-shot classification and unsupervised text style transfer show superior performance over a wide range of existing finetuning or prompting methods.
  arXiv  Detail & Related papers  (2022-05-25T07:50:31Z)
• Instance-aware Prompt Learning for Language Understanding and Generation [49.22899822734549]
  We propose an instance-aware prompt learning method that learns a different prompt for each instance. Our method achieves the state-of-the-art on the SuperGLUE few-shot learning benchmark.
  arXiv  Detail & Related papers  (2022-01-18T17:03:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.