Session 6 Slides: Rapid Application Prototyping

--:-- --
↓ Scroll for more

Session 6

Rapid Application Prototyping

AI Product Engineering

Block 2: AI-Assisted Engineering & Integration

The Prototype Mindset

A prototype answers: "Can AI do this task well enough to be useful?" with minimum investment.

✅ Yes to

  • Core AI functionality
  • Demonstrable workflow

❌ Not needed yet

  • Production infrastructure
  • Authentication
  • Perfect UI polish

Architecture-First: Three Questions

  1. What does the user do? Describe every interaction step-by-step
  2. Where does processing happen? Client? Server? External API?
  3. What data moves between layers? Strings, JSON objects, files?

Draw the box-and-arrow diagram before generating any code.

The Minimal Stack

LayerTechnology
FrontendHTML + CSS + vanilla JS
BackendPython + Flask
AI LayerCloud API (Session 7)
KnowledgeObsidian Markdown (Sessions 8–9)

Runs on any laptop. No cloud deployment required during development.

Generating a UI: The Workflow

1. Describe the screen in plain English
2. Specify constraints (no external libraries, mobile-responsive)
3. Generate with Copilot or chat AI
4. Open in browser — evaluate layout and errors
5. Iterate with targeted refinement prompts

The Mock Backend Pattern

Replace the AI call with a hardcoded string. Test the full UI → server → response cycle before the API is integrated.

Isolates UI bugs from AI logic bugs — much easier to debug.

Session 6 Summary

  • Prototype = minimum to validate "can AI do this?"
  • Architecture first — draw before you generate
  • Minimal stack: HTML + Flask, runs anywhere
  • Mock backend lets you test flow before AI integration

Next Session: API Integration & Cloud Models