Show Notes
Parker breaks down how to craft effective PRDs for AI-assisted coding, how to run a focused planning session with a technical product mindset, and how to turn that plan into real, shippable tooling using a starter kit approach.
PRD fundamentals: what, why, how
- Core idea: a modern PRD for coding with LLMs combines what you're building, why it matters, and how you’ll build it.
- The simplest version:
- What: the product or feature.
- Why: the customer/problem you're solving.
- How: the plan to implement (with real constraints and decisions).
- From problem to solution:
- Start with a clear problem statement (not a list of features).
- Define the solution at a high level, then identify no-go constraints and “nos” to avoid rabbit holes.
- Include open questions to capture missing context before coding starts.
- Roles to remember:
- Product Manager vs. Project Manager distinction matters for PRD quality; the PRD should drive what and why, while the architecture guides how.
Modern PRD with LLMs: process and collaboration
- Two-people dynamic: mix of product thinking (what/why) with technical depth (how).
- The braintrust: a technical product manager plus a 10x engineer-like perspective to challenge assumptions and force corner cases.
- Structured thinking tools:
- Mental jousting sessions to surface landmines and edge cases.
- Five whys to keep asking why a feature is needed and what problem it truly solves.
- Domain-based architecture mindset:
- Start from data and domain needs, work up to UI.
- Gather data points, define data flows, then map to frontend components.
- How to run it:
- Build a PRD workshop, expand prompts to capture all details, then compress to the essentials for execution.
Turbo Kit: a practical starter kit for newbies
- Goals: make it easy to go from training wheels to a real app with AI help.
- Stack concepts:
- Database: Supabase (DB + API layer).
- Data fetching and API patterns: simple Next.js approach (with options like TRPC).
- UI: ShadCN for rapid, consistent components.
- Practical add-ons:
- A chat bot in the PRD to answer questions and keep the team aligned.
- Clear scope management to avoid feature bloat or scope creep.
From idea to PRD: concrete structure and example
- Typical PRD sections to fill:
- Introduction / pitch: a tight problem statement.
- Goals and success criteria: what “done” looks like.
- User stories: who gains value and how.
- Functional requirements: what the system must do.
- Nonfunctional requirements: performance, usability, compatibility, etc.
- Design considerations: UX and technical constraints.
- No-go / out-of-scope items: explicit boundaries.
- Open questions: what you still need to answer.
- Example: custom cursor feature
- Goals, user stories, and a concrete set of styles and hover behaviors.
- Asset handling: SVGs as reusable icons, with a plan to export and reference them cleanly.
- Implementation notes: how to wire icons into your components, how to pass className props, and how to store preferences (localStorage).
- Outcome: a PRD that not only describes the feature but also gives the AI a clear boundary and the questions to resolve.
Workflow: expanding prompts, compressing, and automating
- Expand-to-brief workflow:
- Start with a big prompt describing the project, then expand into detailed PRD sections.
- Use an agent to answer questions and fill in the draft PRD.
- Compress to essentials:
- Drill down to the “juice” that actually guides implementation.
- Remove fluff so AI focuses on concrete tasks.
- Task execution with Taskmaster:
- Parse the PRD into actionable tasks via the Taskmaster CLI.
- Get a structured JSON of tasks, dependencies, and next steps.
- Practical payoff:
- You get a reproducible, automatable path from PRD to kickoff to delivery.
Real-time coding flow: building assets and integrating with the PRD
- Asset work (example): creating and exporting SVG icons for the custom cursor.
- Component wiring:
- Export icons as React components, accept props for styling (className, SVG props).
- Centralize icons in a single module (icons.tsx) for reuse.
- Tailwind and globals:
- Plan to extend Tailwind config for custom cursor classes and map them to SVG assets.
- Use relative paths to keep PRD references portable.
- Outcome: a reusable, scalable cursor system that can be dropped into any page with minimal friction.
Energy, process discipline, and the path to automation
- Musk’s five-step approach (adapted): do it manually, then cut steps, then go faster, then question steps, then automate.
- The current reality: push through the hard manual work first, then automate once the workflow is proven.
- Practical takeaway: don’t automate first; ensure the manual process is rock solid and repeatable.
Community, next steps, and calls to action
- There’s a Discord community for rapid feedback and automations.
- For deeper dive, check the main channel’s content (the Cursor/PRD workflow is explored there).
- Expect a Turbo Kit update with deliverables, templates, and a ready-to-run starter.
Quick actionable takeaways
- Always start with a sharp PRD: focus on problem, then solution, then explicit constraints.
- Use a structured, collaborative workshop with a technical mindset to surface edge cases early.
- Pick a minimal starter stack (Supabase + Next.js + Tailwind + ShadCN) and build a chat-assisted PRD to keep questions flowing.
- Convert PRD to tasks with Taskmaster to avoid scope drift and accelerate execution.
- Build reusable UI assets (SVG icons) as components with flexible styling to maximize reuse.