lightbulbPractical problemLLM Prompt Engineering and Authoring Tools
Developers struggle to optimize prompt design, leading to low accuracy in emotion and mental health tasks.LLM and Natural Language Interaction / LLM Prompt Engineering and Authoring Tools
Developers struggle to optimize prompt design, leading to low accuracy in emotion and mental health tasks.
Similar questions
lightbulbPractical problemLLM Prompt Engineering and Authoring Tools
Users cannot efficiently evaluate and optimize LLM instruction effectiveness in complex tasks.lightbulbPractical problemLLM Prompt Engineering and Authoring Tools
Data engineers and business analysts face inconsistent interfaces when integrating and processing complex data.lightbulbPractical problemLLM Prompt Engineering and Authoring Tools
Users' feedback on LLMs is difficult to sustain in model behavior, requiring repeated actions across interactions.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
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.Related papers
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