# Towards Detecting Prompt Knowledge Gaps for Improved LLM-guided Issue Resolution

arXiv: https://arxiv.org/abs/2501.11709 · HTML: https://mlsanity.com/paper/2501.11709/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jan 2025.

## Built on by (most cited first, 11 of 11)

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- [When Agents Fail: A Comprehensive Study of Bugs in LLM Agents with Automated Labeling](https://mlsanity.com/paper/2601.15232/) (2026)
- [Understanding Prompt Management in GitHub Repositories: A Call for Best Practices](https://mlsanity.com/paper/2509.12421/) (2025)
- [From Prompts to Propositions: A Logic-Based Lens on Student-LLM Interactions](https://mlsanity.com/paper/2504.18691/) (2025)
- [Code Roulette: How Prompt Variability Affects LLM Code Generation](https://mlsanity.com/paper/2506.10204/) (2025)
- [Towards Conversational Development Environments: Using Theory-of-Mind and Multi-Agent Architectures for Requirements Refinement](https://mlsanity.com/paper/2505.20973/) (2025)
- [Generative AI for Managerial Decision-Making under Ambiguity and Sycophancy](https://mlsanity.com/paper/2603.03970/) (2026)
- [A Taxonomy of Runtime Faults in Model Context Protocol Servers](https://mlsanity.com/paper/2606.05339/) (2026)
- [How Do LLMs Read Bug Reports? An Empirical Study of Attention in LLMs for Automated Program Repair](https://mlsanity.com/paper/2607.25873/) (2026)

## Abstract (arXiv)

Large language models (LLMs) have become essential in software development, especially for issue resolution. However, despite their widespread use, significant challenges persist in the quality of LLM responses to issue resolution queries. LLM interactions often yield incorrect, incomplete, or ambiguous information, largely due to knowledge gaps in prompt design, which can lead to unproductive exchanges and reduced developer productivity. In this paper, we analyze 433 developer-ChatGPT conversations within GitHub issue threads to examine the impact of prompt knowledge gaps and conversation styles on issue resolution. We identify four main knowledge gaps in developer prompts: Missing Context, Missing Specifications, Multiple Context, and Unclear Instructions. Assuming that conversations within closed issues contributed to successful resolutions while those in open issues did not, we find that ineffective conversations contain knowledge gaps in 44.6% of prompts, compared to only 12.6% in effective ones. Additionally, we observe seven distinct conversational styles, with Directive Prompting, Chain of Thought, and Responsive Feedback being the most prevalent. We find that knowledge gaps are present in all styles of conversations, with Missing Context being the most repeated challenge developers face in issue-resolution conversations. Based on our analysis, we identify key textual and code-related heuristics (Specificity, Contextual Richness, and Clarity) that are associated with successful issue closure and help assess prompt quality. These heuristics lay the foundation for an automated tool that can dynamically flag unclear prompts and suggest structured improvements. To test feasibility, we developed a lightweight browser extension prototype for detecting prompt gaps, that can be easily adapted to other tools within developer workflows.