Low-code platforms enable rapid development of complex mission-critical software applications. High-level abstractions accompanied by AI assistance allow users with less technical backgrounds to become proficient developers.

Technical debt is the cost of additional rework in software development caused by choosing a fast delivery over maintainability. Even though low-code abstracts significantly complex, projects can still suffer from technical debt. OutSystems guides developers through the AI Mentor System (AIMS), a centralized platform to monitor code quality.

This paper explores the application of state of the art tools in an industry setting of low-code editors. OutSystems has users with and without technical background, providing new challenges and mixed user feedback. We address the challenges of managing technical debt, where users have a wide range of experience, focusing on the results of the different patterns present in AIMS since its release in 2017. For instance, the usefulness of a pattern is determined not only by its correct detection, as it can have an unclear path (or be time-consuming) for refactoring. In the paper, we deep dive into the feedback and insights focusing primarily on two patterns: duplicated code and missing descriptions. Finally, we discuss the broader challenges of developing and evolving AIMS itself, particularly in the context of the mental models and expectations that low-code users bring.