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LLM and Natural Language Interaction / LLM Prompt Engineering and Authoring Tools

Lay users struggle to obtain accurate and easy-to-use help guidance when using complex software.

lightbulbPractical problemLLM Prompt Engineering and Authoring Tools

Lay users struggle to obtain accurate and easy-to-use help guidance when using complex software.

IUI '24Why and When LLM-Based Assistants Fail: Exploring the Effectiveness of Prompt-Based Interaction for Software Help-Seeking

Similar questions

lightbulbPractical problemLLM Prompt Engineering and Authoring Tools

Users cannot efficiently evaluate and optimize LLM instruction effectiveness in complex tasks.

IUI '25CoPrompter: User-Centric Evaluation of LM Instruction Alignment for Improved Prompt Engineering

lightbulbPractical problemLLM Prompt Engineering and Authoring Tools

Developers struggle to optimize prompt design, leading to low accuracy in emotion and mental health tasks.

CHI '25Exploring Modular Prompt Design for Emotion and Mental Health Recognition

lightbulbPractical problemLLM Prompt Engineering and Authoring Tools

Data engineers and business analysts face inconsistent interfaces when integrating and processing complex data.

IUI '24Empirical Evidence on Conversational Control of GUI in Semantic Automation

lightbulbPractical problemLLM Prompt Engineering and Authoring Tools

Users' feedback on LLMs is difficult to sustain in model behavior, requiring repeated actions across interactions.

IUI '24ConstitutionMaker: Interactively Critiquing Large Language Models by Converting Feedback into Principles

lightbulbPractical problemLLM Prompt Engineering and Authoring Tools

Users struggle to design and optimize LLM prompt chains from scratch because the process is complex and laborious.

UIST '24ChainBuddy: An AI-assisted Agent System for Helping Users Set up LLM Pipelines

lightbulbPractical problemLLM Prompt Engineering and Authoring Tools

Ordinary users struggle to design effective prompts and optimize LLM output.

CHI '23Why Johnny Can’t Prompt: How Non-AI Experts Try (and Fail) to Design LLM Prompts

Related papers

CoPrompter: User-Centric Evaluation of LM Instruction Alignment for Improved Prompt Engineering

Ishika Joshi, Simra Shahid, Shreeya Manasvi Venneti

Exploring Modular Prompt Design for Emotion and Mental Health Recognition

Minseo Kim, Taemin Kim, Thu Hoang Anh Vo

Empirical Evidence on Conversational Control of GUI in Semantic Automation

Daniel Karl I. Weidele, Mauro Martino, Abel N. Valente

ConstitutionMaker: Interactively Critiquing Large Language Models by Converting Feedback into Principles

Savvas Petridis, Benjamin D Wedin, James Wexler

Why Johnny Can’t Prompt: How Non-AI Experts Try (and Fail) to Design LLM Prompts

J.D. Zamfirescu-Pereira, Richmond Y. Wong, Bjoern Hartmann

AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts

Tongshuang Wu, Michael Terry, Carrie J Cai

Great Chain of Agents: The Role of Metaphorical Representation of Agents in Conversational Crowdsourcing

Ji-Youn Jung, Sihang Qiu, Alessandro Bozzon

Design Guidelines for Prompt Engineering Text-to-Image Generative Models

Vivian Liu, Lydia B Chilton