Loading image: Build an MVP with AI Coding Tools - Verlin Labs AI article cover image…Build an MVP with AI Coding Tools
AI coding tools compress the path from idea to working prototype - if you scope ruthlessly and verify constantly. This workshop guide walks through problem framing, stack selection, pair-programming patterns with AI, testing habits, and what to demo on day one.
Verified learning path
This workshop connects to live Verlin Labs programs - not generic AI content. Apply these frameworks in a cohort with mentor feedback and a capstone demo.
Scope the smallest demo
Pick one user action that proves value: sign up, submit a form, see a result. Cut integrations, admin panels, and pixel-perfect design for v0. Write a one-paragraph spec and a five-item acceptance checklist before opening the IDE.
AI tools generate code faster than they generate correct requirements - your clarity is the bottleneck.
Stack and workflow
Choose boring, well-documented stacks (Next.js, Supabase, Vercel) so the model has training signal. Work in small slices: generate, run, test, commit. Ask AI to explain diffs, not just dump files. Never merge code you cannot explain in a sentence.
- Keep a running TEST.md of manual checks.
- Use type systems and linters - they catch AI mistakes cheaply.
- Store API keys in environment variables, never in chat prompts.
Demo day discipline
Record a two-minute walkthrough of the happy path. List known bugs openly. Stakeholders fund learning from demos that show real clicks, not slide decks. Plan the second sprint based on what broke during the demo.
Key takeaway
AI coding tools multiply execution speed - not product judgment. Narrow scope, verify each slice, and demo real clicks; that is how MVPs become learning engines instead of fragile demos.
