Configurable AI Coding Assistants: Designing For Developers Who Like to Be in Control
For designers of AI coding assistants, this paper identifies specific configuration needs and preferences of professional developers, though the findings are based on a limited sample and are incremental.
The paper investigates what configurations experienced developers want in AI coding assistants, finding that 72.6% of usefulness ratings are positive but only a third of configurations are known to participants. Developers prioritize task-related controls like confidence thresholds and suggestion quality over persona-related settings.
AI coding assistants are now widely used in professional development, yet they offer only limited ways for developers to control how they behave. In this paper, we investigate what kinds of configurations experienced developers want in coding assistants, how they prioritize different types of configuration needs, and which interface mechanisms they prefer. We first synthesize product documentation and prior research on trust and personalization to compile a list of 33 configuration options, grouped into four categories: Code suggestions, System & policies, Human-assistant interaction, and Users & their personal context. We then conduct a survey with 56 professional developers and 7 design sessions in which participants arrange configurations into their perfect control board and talk about their needs and experiences in more depth. Developers report strong interest in configurability: 72.6% of usefulness ratings are positive, while only around a third indicate that the corresponding configuration is known to participants in their tools. Demand is particularly high for task-related controls such as minimum confidence thresholds, visibility of suggestion quality, and response length, whereas many persona-related configurations are seen as unnecessary. In this paper, we discuss the implications for designing more unified and discoverable configuration surfaces for future coding assistants