Most people arent failing with AI because it’s weak.. They’re failing because they treat it like magic instead of engineering
Ive built production apps this way
Real users. Real traffic. Real consequences.
Mostly with Cursor. Very little manual intervention
But first… this is likely your current flow:
You open your editor
You type “build me X”
AI starts strong… then drifts
One fix breaks another thing
You restart. Again
That’s not building
That’s rolling dice!!
Here’s the system I use
It’s boring. It’s structured
And it works every single time
Step 1 : architecture first (before a single line of code)
Before touching Cursor, open ChatGPT and ask for structure, not code
Describe the product in painful detail
What it does. Who its for. What matters. What doesnt.
Then ask for:
the full architecture
folder and file structure
what each part is responsible for
where state lives
how things talk to each other
This document is the spine of the app
If this is vague, everything downstream will be vague too
Step 2 : turn the architecture into small boring tasks
Next ask AI to convert that architecture into a task list
Not “build auth”
But “create auth schema”, “wire session state”, “protect route X”..
Each task must:
be small enough to test
have a clear start and end
touch one concern only
The key detail: tell the AI these tasks will be executed one by one with testing in between
This becomes http://tasks.md
At this point, you still havent written code
But the chaos is already gone
Step 3 : now I let Cursor work (with rules)
Only now open Cursor
tell it:
You’re an engineer joining this project
You’ve been given http://architecture.md and http://tasks.md
Read them carefully. No guessing
Then add strict rules:
minimal code only
no refactors unless asked
no unrelated changes
don’t break existing behavior
stop after each task so I can test
One task
Run it
Test it
Commit it
Repeat
This sounds slower
It’s not
Why this works (and vibe coding usually doesnt)
Most vibe coding fails for one reason: intent isn’t frozen
When intent is fuzzy AI fills gaps with guesses
Those guesses compound
Thats how you get “it worked yesterday” bugs
This workflow fixes that
You’re not dumping everything into the IDE and hoping
You’re giving AI a map
You’re keeping it on rails
You stay the one making decisions
AI becomes a fast, obedient engineer
Not a creative wildcard.
This is how you ship clean, testable, AI assisted code
without the spiral.. without rewrites and without fear of touching things later
Id normally say “follow me for the playbook” but f it.. just use it
What do you do when managing context takes longer than doing everything by hand? For example, for an existing project where reading a Jira ticket may not suffice and you need to examine unbounded amount of something else, which is usually kept in developer head? Do you begin to treat that as new kind of code/documentation debt that only matters with these new tools who are always amnesiac? How much do MDs really scale?
I’ll say it straight
Most people arent failing with AI because it’s weak.. They’re failing because they treat it like magic instead of engineering
Ive built production apps this way Real users. Real traffic. Real consequences. Mostly with Cursor. Very little manual intervention
But first… this is likely your current flow:
You open your editor You type “build me X” AI starts strong… then drifts One fix breaks another thing You restart. Again That’s not building That’s rolling dice!!
Here’s the system I use It’s boring. It’s structured And it works every single time
Step 1 : architecture first (before a single line of code)
Before touching Cursor, open ChatGPT and ask for structure, not code
Describe the product in painful detail What it does. Who its for. What matters. What doesnt. Then ask for:
the full architecture folder and file structure what each part is responsible for where state lives how things talk to each other
Nothing fancy. Markdown only
Save this as http://architecture.md and drop it into an empty project folder
This document is the spine of the app If this is vague, everything downstream will be vague too
Step 2 : turn the architecture into small boring tasks
Next ask AI to convert that architecture into a task list
Not “build auth” But “create auth schema”, “wire session state”, “protect route X”.. Each task must:
be small enough to test have a clear start and end touch one concern only
The key detail: tell the AI these tasks will be executed one by one with testing in between
This becomes http://tasks.md At this point, you still havent written code But the chaos is already gone
Step 3 : now I let Cursor work (with rules)
Only now open Cursor
tell it: You’re an engineer joining this project You’ve been given http://architecture.md and http://tasks.md Read them carefully. No guessing Then add strict rules:
minimal code only no refactors unless asked no unrelated changes don’t break existing behavior stop after each task so I can test
One task Run it Test it Commit it Repeat
This sounds slower It’s not
Why this works (and vibe coding usually doesnt)
Most vibe coding fails for one reason: intent isn’t frozen
When intent is fuzzy AI fills gaps with guesses Those guesses compound Thats how you get “it worked yesterday” bugs
This workflow fixes that You’re not dumping everything into the IDE and hoping You’re giving AI a map You’re keeping it on rails You stay the one making decisions AI becomes a fast, obedient engineer Not a creative wildcard.
This is how you ship clean, testable, AI assisted code without the spiral.. without rewrites and without fear of touching things later
Id normally say “follow me for the playbook” but f it.. just use it
What do you do when managing context takes longer than doing everything by hand? For example, for an existing project where reading a Jira ticket may not suffice and you need to examine unbounded amount of something else, which is usually kept in developer head? Do you begin to treat that as new kind of code/documentation debt that only matters with these new tools who are always amnesiac? How much do MDs really scale?