Designers should resist the urge to create fully functional, pixel-perfect components in AI prototyping tools. This work is often throwaway because it doesn't leverage the actual production codebase or design system, leading to wasted time and effort.
Before writing any code for a complex feature or bug fix, delegate the initial discovery phase to an AI. Task it with researching the current state of the codebase to understand existing logic and potential challenges. This front-loads research and leads to a more informed, efficient approach.
Waiting for a single AI assistant to process requests creates constant start-stop interruptions. Using a tool like Conductor to run multiple AI coding agents in parallel on different tasks eliminates this downtime, helping developers and designers maintain a state of deep focus and productivity.
For designers who code but aren't senior engineers, submitting pull requests can be daunting. Using an integrated AI code review agent provides an extra layer of validation. It catches potential issues and suggests improvements, boosting confidence before the code undergoes human review.
For large projects, use a high-level AI (like Claude's Mac app) as a strategic partner to break down the work and write prompts for a code-execution AI (like Conductor). This 'CTO' AI can then evaluate the generated code, creating a powerful, multi-layered workflow for complex development.
A mental model for selecting AI tools based on two axes: the size of the task (from a small bug fix to a large new feature) and the amount of code that already exists in production. This framework helps designers decide when to use a prototyping tool versus a production-focused AI agent.