Instead of fighting for perfect code upfront, accept that AI assistants can generate verbose code. Build a dedicated "refactoring" phase into your process, using AI with specific rules to clean up and restructure the initial output. This allows you to actively manage technical debt created by AI-powered speed.
To prevent AI coding assistants from hallucinating, developer Terry Lynn uses a two-step process. First, an AI generates a Product Requirements Document (PRD). Then, a separate AI "reviewer" rates the PRD's clarity out of 10, identifying gaps before any code is written, ensuring a higher rate of successful execution.
To ensure comprehension of AI-generated code, developer Terry Lynn created a "rubber duck" rule in his AI tool. This prompts the AI to explain code sections and even create pop quizzes about specific functions. This turns the development process into an active learning tool, ensuring he deeply understands the code he's shipping.
To validate his fitness tracker idea, developer Terry Lynn first used the Apple Watch's voice memo app to record workouts. He then wrote a simple Python script to process the audio files with GPT-4 and output the structured data into a spreadsheet. This ultra-lean MVP tested the core concept without the overhead of app development.
Human developers may prefer longer files, but AI coding assistants process code in smaller chunks. App developer Terry Lynn intentionally keeps his files small (under 400 lines) to reduce the AI's context window usage, prevent it from getting lost, and improve the speed and accuracy of its code generation.
When an AI coding assistant goes off track, it can be hard to undo the damage. Developer Terry Lynn mitigates this risk by programming his AI workflow to make a Git commit before and after each small phase of a task. This creates a trail of "breadcrumbs," allowing him to easily revert to a stable state if the AI makes a mistake.
To rapidly iterate on mobile UI, Lynn sketches screens on physical index cards, which have a similar aspect ratio to a phone. He then photographs these low-fidelity mockups and uses GPT-4's image generation to "upscale" them into high-fidelity designs, bridging the gap between physical brainstorming and digital prototyping tools like Figma.