This is the workflow I used during the Kiro hackathon to take an idea from spark → MVP in under a week, with the help of an AI coding assistant. Think of it as a blend of brainstorming, system‑level checks, and ruthless pruning.
Why speed matters
Hackathons aren’t forgiving. You don’t have months to debate features — you have days. Most projects fail because they:
- Jump straight into code without testing assumptions.
- Skip over system blockers (permissions, manifests, API quotas).
- Have no clear metric for success.
My approach flips that. Instead of coding first, I battle the idea, refine it into a lean PRD(Product requirements documentation), and only then let the AI build from a checklist.
The 5‑step workflow
1) Start with an idea + kill criterion
Write one sentence eg. : For [user], who needs [problem], our app [solution or service]. This directs you to pin down who it's for and what function it serves.
Then set a kill criterion — if feasibility isn’t proven in 48 hours, pivot. This prevents sunk‑cost traps.
Also keeping in mind this is coding on Steroids , so the half-baked product is easily disposable since there is no sunk-cost traps.
2) Interrogate the idea with an AI assistant
Don’t just brainstorm features. Ask the AI to list:
- The good,bad and ugly.
- Authentication needs (OAuth, API keys, token refresh).
- Third‑party services & quotas.
- Likely failure modes.
📌 Pro tip: force the AI to play devil’s advocate. The sooner flaws surface, the easier they are to fix.
3) Refine into a PRD.md + checklist
After debating with the Ai assistant, instruct it to generate
a verbose instructional PRD that a developer or an LLM would use to create a full product. The PRD should be structured in a workflow step by step with a checklist.
4) Build from the PRD — not from memory
Hand the PRD to the AI and request:
- Task list with check-boxes.
- Instruct it to mark a checkbox after completing a step.
This way, the AI builds within clear constraints — not free‑wheeling guesswork.
5) Debug, document, repeat
Debug system blockers first: manifest issues, provisioning, API keys.
When your app is fully created, now debug for launch problems which mostly is minor if you follow the outlined steps above.
Generate on-boarding docs:
You need now to instruct the AI to generate a full verbose documentation of your app. You will need this to save quotas for a new session instead of pushing your whole code to the context length again.Preferably prompt
_" Generate a full documentation for the app that a developer or LLM could use to upgrade features, use the PRD and the source code."_
- An Extra impactful step
Store docs in the repo.
In new sessions, ask the AI to “read the documentation” before coding.
📌 Always update the documentation when you change code — this keeps the AI and future devs in sync.
i.e
When you make feature changes always instruct the AI to update relevant docs.
Pitfalls I hit (so you don’t)
AI code isn’t infallible. It can hallucinate APIs or miss a permission. since you have a PRD and documentation, you can easily clear the session and track you progress.
Costs add up fast. Simulate API spend early.
Design still needs humans. The AI can scaffold, but UI/UX taste is on you.
📌Always use git
Why Kiro fit perfectly
Kiro made this loop easier: brainstorm → interrogate → PRD → build → debug → doc. Because the workflow is modular, I could adapt fast, keep track of changes, and avoid the chaos most weekend hack projects drown in.
By the end, I had a working, documented MVP — not just a pile of code.
Closing thought
With a structured workflow and AI discipline, “one week to MVP” isn’t hype. It’s repeatable. The real trick is forcing clarity early and never skipping the checklist.
**Feel free to leave a comment or a query!**
Top comments (0)