howto AI design

The right move is to componentise first, then progressively deepen each component to the level an AI coding agent can execute without ambiguity. A single high-level narrative is useful for vision, but insufficient for reliable code generation.

Recommended approach (staged and pragmatic):

  • Freeze the product boundary (one-page overview) ✅2025-12-20 see PRODUCT
  • Decompose into first-order components ✅2025-12-20 see COMPONENTS

1. Freeze the product boundary (one-page overview)

Start with a short, stable document that answers only:

  • What the product is (Substack-like publishing for data)
  • What it is explicitly not (no notebooks, no heavy portals, no bespoke dashboards)
  • The core unit (publication → data posts) This document should not change much; it is for alignment, not implementation.

2. Decompose into first-order components

Split the system into orthogonal concerns that can be reasoned about independently. For this product, a clean cut is:

  1. Identity and accounts
  2. Publications
  3. Data posts and ingestion
  4. Social graph (likes, follows, subscriptions)
  5. Payments and access control
  6. Analytics and notifications
  7. Distribution (email, feeds, URLs)

Each component should have:

  • Clear inputs and outputs
  • Minimal shared state
  • A well-defined owner (conceptually)

This is crucial for AI planning because large models struggle with tightly coupled, cross-cutting logic.

3. Write a spec per component, not per feature

For each component, create a short spec with the same internal structure:

a. Purpose

What job this component serves in isolation.

b. Core entities

Data models only (e.g. Publication, DataPost, Subscription), with fields and invariants.

c. Primary flows

Happy-path flows only. No edge cases yet.

d. Public interfaces

APIs, events, or functions this component exposes to others.

e. Non-goals

What this component explicitly does not handle.

This level of specificity is where AI coding becomes reliable.

4. Only then add cross-cutting flows

Once components are specified, define a small number of end-to-end flows, such as:

  • “New user → creates publication → publishes first data post → gets first subscriber” These flows act as integration tests for the architecture and help an AI reason about glue code.

5. Delay optimisation and edge cases

Do not spec:

  • Performance tuning
  • Advanced permissions
  • Migration, imports, or power-user features

AI systems degrade rapidly when asked to plan for hypothetical future complexity. Treat everything beyond MVP as explicit future work.

6. Use a repeatable prompt pattern for AI coding

When you move to implementation, give the AI:

  • One component spec at a time
  • One task at a time (e.g. “implement data models + CRUD for Publications”)
  • Explicit constraints (framework, language, storage)

Avoid asking an AI to “build the app.” Ask it to “build this component according to this spec.”

Bottom line

Yes: componentise first, then deepen each component just enough to remove ambiguity. High-level vision guides direction, but AI coding succeeds when each piece is small, bounded, and boring.

Raw

Okay, how would you go about developing this into a spec, or for an app, to actually build the app with AI coding? Do I need to possibly split this up into sub-components and specify them in more detail, like separate this into key different concerns, or stay with a high-level overview, and how do we distill this high-level overview and maybe spec out some of the details that would get asked? What would be your recommended approach here? Would it be first to componentize and clearly separate the major components of this application that can then be developed and detailed differently, given the limitations and capacity of AI planning?