Beyond Basic Prompts: Crafting Self-Improving & Modular Rules for Your AI Coding Assistant
In my previous post, I touched upon the initial realization that AI, for all its power, needs clear guardrails and context to be truly effective in software development. Early experiments with tools like cursor-memory-bank showed promise but also highlighted the need for more sophisticated guidance than a simple memory dump. This led me down the path of exploring how to craft better “rules” for my AI coding assistant – rules that are not just static instructions, but part of a more dynamic and even self-improving system.
The Spark: Self-Improving Rules
A significant step in this direction came with the discovery of projects like rules generator. The idea of an AI agent that could help refine its own operational standards was particularly appealing. Inspired by this, specifically the core-rules/rule-generating-agent.mdc file within that project, I developed my own rule-generating-standarts-agent.mdc (Note: this link would be relative to the original project structure, adjust if needed for the blog). The goal was to create a foundational set of standards that could evolve.
The “Rules” Conundrum: A Universal Language?
However, a challenge quickly emerged: what exactly is a “rule” in the context of AI assistants? While platforms like Roo, Cline, and Cursor all utilize system prompts (essentially plain text instructions) to guide the AI, their interpretation, application, and the surrounding tooling can differ significantly. This divergence means that a rule set meticulously crafted for one environment might not translate seamlessly to another.
This realization underscored the need for a modular approach to rule design. If the core logic of a rule could be defined agnostically, it could then be adapted or wrapped for specific platforms, promoting reusability and easing potential migrations between tools in the future.
Committing to a Platform (For Now): Cursor and Modularity
For the current phase of my exploration, I decided to focus my efforts on implementing rules within the Cursor environment. While doing so, I’ve kept modularity as a key principle, structuring rules with an eye towards eventual adaptation for other systems, perhaps like Roo code.
This decision comes with an awareness of Cursor’s current limitations. For instance, the lack of a /new_task command means that context can become cluttered over long sessions. Similarly, there isn’t an easy, built-in way to switch to “cheaper” or less powerful models for simpler, less context-intensive tasks, which could be an efficiency gain.
When Rules Don’t Stick: The Application Challenge
Even with a chosen platform and a modular design philosophy, getting rules to apply consistently and as intended proved to be another hurdle. There were instances where agent-specific or auto-applying rules didn’t trigger correctly, despite clear descriptions.
After several attempts to refine rule descriptions and coax the AI, I realized that a more explicit management system might be necessary, at least for now. This led to the idea of creating a rule registry. Such a registry would not only help in debugging and ensuring rules are loaded and applied correctly but also significantly simplify any future migration efforts. Instead of the AI trying to infer which rules to use based on file paths or names (which proved unreliable), a registry could provide a more deterministic way to manage and invoke them.
The journey to create a truly intelligent and seamlessly integrated AI coding assistant is iterative. Moving beyond basic prompts into the realm of structured, modular, and potentially self-refining rules is a critical step, but one that comes with its own set of platform-specific and conceptual challenges.