Why and When LLM-Based Assistants Can Go Wrong: Investigating the
  Effectiveness of Prompt-Based Interactions for Software Help-Seeking
Anjali Khurana Hari Subramonyam Parmit K Chilana
Large Language Model (LLM) assistants, such as ChatGPT, have emerged as
potential alternatives to search methods for helping users navigate complex,
feature-rich software. LLMs use vast training data from domain-specific texts,
software manuals, and code repositories to mimic human-like interactions,
offering tailored assistance, including step-by-step instructions. In this
work, we investigated LLM-generated software guidance through a within-subject
experiment with 16 participants and follow-up interviews. We compared a
baseline LLM assistant with an LLM optimized for particular software contexts,
SoftAIBot, which also offered guidelines for constructing appropriate prompts.
We assessed task completion, perceived accuracy, relevance, and trust.
Surprisingly, although SoftAIBot outperformed the baseline LLM, our results
revealed no significant difference in LLM usage and user perceptions with or
without prompt guidelines and the integration of domain context. Most users
struggled to understand how the prompt's text related to the LLM's responses
and often followed the LLM's suggestions verbatim, even if they were incorrect.
This resulted in difficulties when using the LLM's advice for software tasks,
leading to low task completion rates. Our detailed analysis also revealed that
users remained unaware of inaccuracies in the LLM's responses, indicating a gap
between their lack of software expertise and their ability to evaluate the
LLM's assistance. With the growing push for designing domain-specific LLM
assistants, we emphasize the importance of incorporating explainable,
context-aware cues into LLMs to help users understand prompt-based
interactions, identify biases, and maximize the utility of LLM assistants.