Show Notes
The big takeaway: AI coding tools will get better, but your ability to shape inputs, define product goals, and chain prompts is the real bottleneck. Build a solid, Markdown-first workflow and own the thinking.
The fatal flaw with popular AI coding tools
- Tools improve, but competency doesn’t magically appear. Models get better; you must still guide them.
- People chase “better” tools instead of building a solid workflow. This leads to fat between tool calls and degraded outputs over turns.
- Real value comes from how you structure prompts, not from relying on a single assistant or gadget.
A practical workflow you can ship with today
- Start with an idea prompt that captures the pitch and responsibility.
- Turn it into a clear PRD (Product Requirements Document).
- Generate an architecture prompt to outline the project’s file structure and tech choices.
- Use system patterns to enforce self-healing rules and consistency (context-aware constraints tied to your codebase).
- Move to PRD+, architecture, and tests in a chained sequence.
- Keep everything around Markdown prompts and a lean CLI wrapper to avoid tool bloat.
Markdown + primitives: the power of simple inputs
- Prefer Markdown prompts to reduce ambiguity and parsing overhead.
- Build a tiny CLI that passes Markdown-based prompts through the chain, instead of heavy tool integrations.
- Keeping inputs simple improves model outputs; complexity adds noise and waste.
Why you should still own the thinking
- Don’t skip the hard work of thinking through the problem before prompting.
- Multi-turn prompts often degrade quality; design, then prompt.
- Taskmaster-type tools can demo capabilities, but they can also mask skill gaps.
The chained-prompt concept (PRD → Architecture → System Patterns → Tests)
- Idea prompt: capture the core concept and deliverable.
- PRD prompt: translate the idea into concrete features and success criteria.
- Architecture prompt: propose file trees and tech structure (Python/TypeScript focus).
- System patterns: define self-healing rules that keep the codebase coherent as it evolves.
- Tests: cover common libraries and edge cases; stay within token budgets (40% of context window as a guardrail).
- The chain is designed to be repeatable and codified for consistency across projects.
The future I’m aiming for
- A pattern where you stay in one workspace, and prompts move the project forward in a linear, verifiable way.
- A combination of PRD, architecture, and system rules that auto-update as the codebase changes.
- Potential cloud-code wave (Gemini/Cloud IDE style) to remove current tool deficiencies and keep a tight feedback loop.
Practical takeaways you can apply now
- Build a small, open CLI around Markdown prompts to chain your steps.
- Focus on creating real inputs: a thorough PRD and a concrete architecture plan before worrying about fine-tuning prompts.
- Use markdown as the lingua franca for prompts; avoid overcomplicating with nested tool calls.
- Don’t tool-hop for a week; it’s a meta-skill you’ll outgrow—learn by building.
- If you have training wheels, use them, but don’t rely on them to replace critical thinking.
- Read and apply concepts from the High Growth Handbook to sharpen your product requirements and execution discipline.
Community, momentum, and next steps
- The daily community is where experiments live and learnings compound.
- If you found this helpful, subscribe and share your thoughts to keep the momentum going.
- The goal is to build smarter, more capable coding workflows that actually ship, not just demo.
Links
- High Growth Handbook by Elad Gil - Guide to scaling startups
- Task Master - AI task management tool
- Cursor - AI-powered code editor and agent workflow tool
- OpenAI Platform - AI SDK and tooling
- Google Gemini CLI - Cloud code for AI development
- VI AI Community - Parker Rex community discussions