Show Notes
In this live, hands-on look, Parker shows how to use AI as a tool without letting it atrophy your problem-solving skills. He walks through concrete workflows, patterns, and utilities that keep you sharp while AI handles the routine bits.
Read the code, not just the AI output
- Always read the generated code. AI can create code you don’t fully understand or that lacks the full context.
- Use reading as a debugging discipline: you’ll spot type issues, edge cases, and integration gaps that the AI might miss.
- Don’t rely on “one-and-done” AI fixes. If you don’t trace and validate, you’ll flip-flop and waste time.
Treat AI like a pair programmer
- Use AI as a teammate who can draft, brainstorm, and refactor, but you drive the conversation and decisions.
- Ask targeted questions to surface alternatives, tradeoffs, and better architectural choices.
How Parker asks the right questions
- What are other ways to write this code? Pros and cons of each approach.
- How would I explain this change to a teammate over the phone? A good litmus test for clarity.
- How can this be refactored to be more modular, readable, and robust?
- What are common mistakes people make with this pattern or feature?
The debugging workflow in a real project
- The project example: a Google Calendar meets Health app called Map.
- Distinguish between UI state vs data state:
- Calendar UI Context: manages selected date, current view, UI flags.
- Calendar Data Context: handles data fetching, events, and caching.
- Use AI to propose architectural patterns, then validate by reading and integrating with your codebase.
- Downtime happens. Build the habit of understanding the full picture so you’re productive even when AI is unavailable.
Contexts, caching, and your code structure
- Clear separation of concerns helps you debug faster when AI isn’t in the loop.
- A caching layer (inspired by the Midday pattern) can dramatically improve performance but requires careful handling so AI changes don’t break the flow.
The repo pack trick
- Repo pack lets you condense your repo into a format AI can read easily—great for onboarding, code review, or large refactors.
- How it works (high level):
- Scans your repo, estimates tokens, and produces a readable summary/file.
- Useful for feeding code into LLMs for analysis, docs, or refactoring ideas.
- Practical uses:
- Open-source project onboarding
- Understanding unfamiliar codebases quickly
- Generating docs or quality checks based on the entire project
The aai prompts directory and per-project prompts
- Maintain an aai (AI-assisted guidance) directory with prompts tailored to your project.
- Example use: SQL prompts for a calendar action. Provide objective, link docs, and specify outputs.
- Benefits: faster, more consistent AI responses and clearer expectations for what the AI should produce.
Add targeted comments to prompts
- Use inline comments in your prompts to speed up AI understanding.
- Clear, explicit prompts reduce back-and-forth and produce more useful results.
- Example approach: describe the calendar action (clear user calendar in Google Calendar and local DB) and return shape, params, and error behavior.
Dictation: speed up prompt drafting
- Built-in dictation (e.g., macOS) is a practical alternative to paid tools.
- Tweak: set up dictation to trigger with a quick shortcut (e.g., Command key twice) and transcribe prompts as you speak.
- Benefit: faster iteration when you’re outlining or explaining code changes.
Practical takeaways and personal workflow
- Don’t rely on AI to solve everything; use it to accelerate learning and reduce drudgery.
- Keep your problem-solving muscles sharp by:
- Reading all AI output
- Validating changes across the full codebase
- Regularly questioning architecture and tradeoffs
- Build a local toolkit:
- Repo pack for repo-wide AI context
- aai prompts directory for project-specific guidance
- Dictation for rapid prompt drafting
Takeaways you can apply today
- Read and audit AI-generated code before integrating it into your project.
- Treat AI as a collaborative partner; push for multiple solutions and explain decisions as if to a teammate.
- Use repo pack to get AI-friendly summaries of your codebase for reviews and onboarding.
- Create an aai prompts directory and keep per-project prompts up to date.
- Add descriptive comments to prompts to speed up AI comprehension.
- Leverage built-in dictation to speed up prompts and explanations.
Links
- Repomix - Tool for packing a repo into an AI-friendly format (formerly Repopack)
- Midday - Open-source business management platform (inspiration for caching pattern)
If you have other tools you’re using to collaborate with AI on code, share them in the comments.