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Large Language Models (LLMs) have gained widespread popularity due to theirability to perform ad-hoc Natural Language Processing (NLP) tasks with a simplenatural language prompt. Part of the appeal for LLMs is their approachabilityto the general public, including individuals with no prior technical experiencein NLP techniques. However, natural language prompts can vary significantly interms of their linguistic structure, context, and other semantics. Modifyingone or more of these aspects can result in significant differences in taskperformance. Non-expert users may find it challenging to identify the changesneeded to improve a prompt, especially when they lack domain-specific knowledgeand lack appropriate feedback. To address this challenge, we present PromptAid,a visual analytics system designed to interactively create, refine, and testprompts through exploration, perturbation, testing, and iteration. PromptAiduses multiple, coordinated visualizations which allow users to improve promptsby using the three strategies: keyword perturbations, paraphrasingperturbations, and obtaining the best set of in-context few-shot examples.PromptAid was designed through an iterative prototyping process involving NLPexperts and was evaluated through quantitative and qualitative assessments forLLMs. Our findings indicate that PromptAid helps users to iterate over prompttemplate alterations with less cognitive overhead, generate diverse promptswith help of recommendations, and analyze the performance of the generatedprompts while surpassing existing state-of-the-art prompting interfaces inperformance.

PromptAid: Prompt Exploration, Perturbation, Testing and Iteration using Visual Analytics for Large Language Models