You asked an AI to build you an app. It looks good and it works. Almost. The moment you touch one feature, another one breaks.
Every time a real user tries it, there's a bug. (No no, don't click that.) The login doesn't actually protect anything. One user's clients show up in another user's dashboard. The payment page collects the credit card but never records the transaction.
Your process is broken. The code is just the symptom.
I built a straw-bale house with my own hands. Five years. The parallel with software (I've been in IT for 30 years) is uncanny: same need for sequence, same need to understand each specialty without becoming a specialist. I turned that into a method so you can develop your app like a pro.
TLDR: Stop telling the AI "build me X." Start specifying what X means, what it implies, how to verify it. Eight steps, same loop every time. Start with your next feature.
The gap nobody talks about
Vibe coding is broken. Not the tools. Not the AI. The way people use it.
Every tutorial shows the same thing: type a prompt, get an app, celebrate. And it works, for a demo. A screen with buttons that look right.
Then you need the app to do real things. Multiple users who each see only their own data. Payments that actually process. A new feature that doesn't silently break three old ones.
The AI optimizes for "works right now." A real app needs "still works in six months." That gap is not about code quality. It's about knowing what to ask for, in what order, and how to check that you got it.
The Blueprint Method
Eight steps. Same loop for every feature, from a simple client list to Stripe payment integration.
1. Global spec. What do you want, in plain language? "I want to manage clients." Vague on purpose. Just enough to start.
2. Implications. The step most people skip, and the one that saves you. Ask the AI: "What does this imply technically? What tables, pages, components do I need?" The AI unpacks your request into a list of concrete things that need to exist. Some you expected. Others (an ID column, timestamps, validation rules) you didn't think of. Now you see the full picture before building anything.
3. Detailed spec. Get specific. Fields, types, validation, error handling, what happens when something goes wrong. This is where "build me a client list" becomes usable.
4. Build. Hand the detailed spec to the AI. Let it build.
5. Explain. Ask the AI: "Explain what you just built and why." You don't read the code. But you understand the flow. This understanding accumulates. By your tenth feature, you have a rich mental model of your entire app.
6. Verify. Check that it works. Not by reading code. By using the app, testing edge cases, and asking the AI "what could go wrong with this?"
7. What can go wrong. Every feature has specific traps. Auth? The AI stores passwords in plain text. Payments? It ignores webhook failures. Security? Overly permissive policies. Name the traps so you catch them.
8. Non-regression. Check that everything from previous features still works. The most common vibe coding failure: adding something new that silently breaks three old things.
The depth increases as you go. Your first spec is two sentences. By the time you're doing payments, it's two pages. Same method. Your skill grows.
A concrete example: authentication
This is where vibe coding fails most spectacularly.
Before.
"Add a login to my app."
The AI builds a page with email and password fields, a button, a redirect. It looks like a login. The UI is fine. But auth is not a UI feature. Auth is infrastructure. The login page is the visible 10%. The other 90% is session management, token validation, route protection, data isolation. The AI skips all of it because you didn't ask.
You ship it. User A logs in. User B logs in. User B sees User A's data.
After.
Same feature, with the method.
Step 1 (global spec): "Each user has their own account. They log in with email and password. Once logged in, they see only their data."
Step 2 (implications): you prompt the AI to explain what this means technically. It comes back with: authentication vs authorization (two different things), sessions, tokens, route protection, and linking data to users (every record needs a user_id column). You didn't know you needed half of this. Now you do. Before writing a single prompt to build anything.
Step 3 (detailed spec): sign up with email confirmation. Login with email and password. Logout button. Session that auto-expires after inactivity. Route protection for ALL pages except login and signup. A user_id column on every data table. Automatic filtering by user on every query.
That's a contract. Every expectation stated, every behavior defined.
Then you build, verify, name the traps (sessions that never expire, user_id checked on INSERT but not on UPDATE or DELETE), and check regression against every previous feature. You know the loop. Same eight steps.
The difference between "add a login" and this is the difference between a door that looks locked and a door that actually is.
The construction sequence
Build in this order:
- Setup and deploy (get something live immediately, even a blank page)
- Understand the building blocks (frontend, backend, database, how they talk)
- Design the interface before building it (vocabulary + consistency rules)
- Your first feature (simple CRUD, learn the loop)
- Data relationships (things that connect to other things)
- Authentication (who are you)
- Data isolation (each user sees only their own data)
- Testing (automate the verification you've been doing by hand)
- Dashboard and polish (make it feel like a product)
- Payments (the feature with the most expensive bugs)
- Go live for real (production deploy, domain, monitoring)
Each step builds on the previous. You can't add authentication before you have pages to protect. You can't add payments before you have users to charge. You can't test effectively before you have features to test.
Every vibe coding disaster I've seen follows the same pattern: someone built the cool feature first (payments, dashboards, fancy UI) and bolted on the boring stuff later (auth, security, error handling). The boring stuff never fits right because it was never part of the architecture.
Skip ahead, and you're wiring electricity in a house with no walls.
The death loop (and how to escape it)
Friday afternoon. You just want to ship one more fix before the weekend. The bug is small. You prompt the AI. The fix works. Except now the sidebar is broken. You prompt again. Sidebar fixed, but the save button doesn't submit anymore. One more prompt. Save works, sidebar broke again, and the login page is a white screen.
Your pool is waiting. Your kids are asking when you're done. You keep prompting. Three rounds ago you had one bug. Now you have four. ðŸ«
That's the death loop.
Each patch fixes one thing and breaks another because the AI keeps treating symptoms. The root cause is still there, buried under layers of quick fixes. You're not debugging anymore. You're making it worse with every prompt.
The exit is always the same. You stop prompting. You revert to the last version that worked (this is why Git exists). And you come back with a different prompt: "Don't fix the symptom. Explain what this error means at the root. I've tried fixing it twice and each fix created new bugs."
Sometimes the feature is too tangled to save. You revert and rebuild it from scratch with a better spec. Sounds painful. Actually faster than a sixth round of whack-a-mole.
Teams get stuck in death loops for weeks. That's why the diagnosis in Bloomberg's AI coding productivity crisis was off: they measured output speed, not loop frequency.
Spec quality is everything
Same feature. Two specs. Two results.
Bad spec: "Build me a client page."
The AI guesses everything. Layout, columns, buttons, what happens when there's no data. The result is random. You didn't specify anything, so the AI filled every blank with its own judgment. Which is often wrong.
Good spec: "Build a client list page with a sidebar navigation. In the main area, a table with columns: name, phone, email, city. A search bar above the table. An 'Add client' button that opens a modal form. When there are no clients, show 'No clients yet, add your first one' with a button. Loading state with a skeleton. Error state with a user-friendly message."
Every element is specified. The result is predictable. And (this is the key) you can verify it, because you know exactly what you asked for.
Bad spec = bad contract. The AI fills the blanks with guesses. Good spec = good contract. Every expectation stated, every behavior defined. I built the full prompt contracts framework after enough features went sideways. Same principle: the spec is the product.
This is learnable. Your first spec will be two sentences. By your tenth, you'll write two pages without thinking about it. The method trains you, one feature at a time.
Spec. Build. Verify. Repeat.
The AI writes all the code. I write zero. But I specify exactly what I want, I understand what was built (without reading code), and I verify everything systematically. That's how I ship production apps with real users, real payments, and data isolation that actually holds.
"Building" and "shipping" are not the same verb.
You asked an AI to build you an app. Now you know how to actually ship it. Eight steps, same loop. Tedious. Not sexy.
Following through is on you.
(*) The cover is AI-generated. The straw-bale house is real though.
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