TLDR: Prompt-with-Me is an IDE plugin that brings structured management to LLM prompts in software engineering. It automatically classifies prompts using a four-dimensional taxonomy, suggests language refinements, masks sensitive data, and extracts reusable templates. A study of 1,108 prompts showed LLMs can accurately classify them, and a user study with 11 developers reported high usability, low cognitive load, and improved prompt quality and efficiency, making prompts first-class software artifacts.
Large Language Models (LLMs) are rapidly changing how software is developed, but managing the prompts used to interact with these models often remains disorganized. This informal approach can lead to issues with reliability, make it hard to reuse prompts, and complicate their integration into professional software development processes. A new solution, called Prompt-with-Me, aims to solve these challenges by providing structured prompt management directly within the developer’s Integrated Development Environment (IDE).
Prompt-with-Me is a practical tool designed to treat prompts as essential software components, much like source code. It automatically organizes prompts using a unique four-dimensional classification system. This system categorizes prompts based on their intent (what the prompt aims to achieve), the author role (who created the prompt, e.g., developer, project manager), the software development lifecycle (SDLC) stage (when the prompt is used, e.g., planning, coding, testing), and the prompt type (how the prompt is structured, e.g., template-based, few-shot). This structured approach helps developers understand, manage, and evolve their prompt libraries more effectively.
Beyond classification, Prompt-with-Me offers several features to improve prompt quality and efficiency. It suggests language refinements to make prompts clearer and more consistent, masks sensitive information to protect privacy, and extracts reusable templates from a developer’s existing prompt library. This means that instead of writing similar prompts from scratch repeatedly, developers can quickly generate new prompts from pre-defined templates, saving significant time and reducing errors.
Evaluating Prompt-with-Me
The effectiveness of Prompt-with-Me was evaluated through two main studies. First, a taxonomy study involving 1,108 real-world prompts demonstrated that modern LLMs can accurately classify software engineering prompts according to the four-dimensional taxonomy. This finding is crucial because it shows that automated systems can reliably understand and categorize the diverse prompts used in software development.
Second, a user study was conducted with 11 software developers. The results were highly positive, showing strong developer acceptance. The system achieved a high usability score (Mean SUS=73), indicating that it is easy and intuitive to use. Participants also reported a low cognitive load (Mean NASA-TLX=21), meaning the tool didn’t add significant mental effort to their tasks. Developers noted significant gains in prompt quality and efficiency, largely due to the reduction of repetitive effort. For instance, many highlighted the template generation feature as particularly useful, as it eliminated the need to copy, paste, or re-type similar prompts repeatedly.
Key Features Explained
Prompt-with-Me includes a suite of processing steps that effectively manage prompts in software development:
Classification: Automatically annotates prompts along the four dimensions (intent, role, SDLC phase, type), providing a structured foundation.
Similarity Detection: Identifies duplicate or near-duplicate prompts using a combination of Levenshtein Distance, Jaccard, and Cosine similarities. This helps keep the library concise and promotes template reuse.
Language Improvement: Performs automated spelling and grammar checks, offering suggestions to enhance clarity and correctness.
Anonymization: Protects sensitive information (like email addresses, API keys, passwords) by redacting it with “[REDACTED]” placeholders, ensuring privacy and security.
Template Generation: Transforms similar prompts into reusable templates with variable placeholders, which can be triggered manually or automatically when duplicates are detected.
Summarization: Provides an overview of the entire prompt library, including topic analysis, intent distribution, and role targeting, to help users quickly understand their prompt collections.
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Insights for Future Tools
The research also provided valuable insights for designing future prompt management tools. Template extraction was identified as a key feature enabling prompt reuse and improving efficiency. Interestingly, while the tool had a low cognitive load, its adaptiveness to specific workflows was found to be a more significant driver for adoption. Future tools should prioritize transparency and reversibility in automated changes, offer domain-specific customization for taxonomies, simplify user interfaces, provide flexible options for privacy (local vs. cloud processing), and include collaborative features for team-based prompt management.
In conclusion, Prompt-with-Me represents a significant step towards making prompt management a systematic and integrated part of software development. By treating prompts as first-class software artifacts, it paves the way for more reliable, reusable, and efficient LLM-assisted software engineering. You can learn more about this innovative tool by reading the full research paper here: Prompt-with-Me: in-IDE Structured Prompt Management for LLM-Driven Software Engineering.
