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Role of AI in This Workflow

This workspace is not trying to prove that AI can replace product, engineering, or QA.

It is trying to prove something narrower and more useful:

AI assistance becomes much more reliable when the workflow is anchored in explicit artifacts.

Intended Use

The expected order is:

  1. Product intent is written in docs/prd/
  2. Execution detail is written in docs/spec/
  3. Backend, frontend, and e2e changes are derived from those docs
  4. Local validation happens through tests, Docker, and k3d

Where AI Helps

  • expanding rough product notes into a structured PRD
  • turning a PRD into a tighter implementation-facing spec
  • identifying backend/frontend/e2e touchpoints from the spec
  • drafting acceptance criteria and validation cases
  • accelerating repetitive implementation scaffolding
  • keeping multi-project changes aligned

Where AI Should Not Lead

  • inventing product rules that are not in the PRD
  • changing API behavior without updating the spec
  • skipping validation just because generated code compiles
  • replacing test intent with vague smoke coverage

Practical Rule

In this workspace, AI should accelerate translation between layers:

  • PRD
  • spec
  • implementation
  • verification

It should not become an unreviewed source of truth.