The 70% Problem with AI-Generated Code
AI gets you to 70% fast. The last 30% is where projects succeed or fail. Here's our framework for handling it.
Every AI coding tool has the same pitch: write code faster. And they deliver — that first 70% comes out shockingly quick.
Then you hit the wall.
The pattern we keep seeing
Sprint one with AI tools feels like magic. Features materialize in hours. The client is thrilled. The team is thrilled.
Sprint two, things slow down. The AI-generated code from sprint one has assumptions baked in that don't quite fit the new requirements. You're editing around decisions you didn't consciously make.
By sprint three, you're spending more time understanding AI code than writing new code. The 70% that felt free now has a maintenance cost.
This isn't a tools problem. It's a process problem.
Why 70% is the wrong target
When AI generates a function, it optimizes for "works for the described case." It doesn't optimize for "fits cleanly into the codebase's patterns" or "handles the edge case we'll discover next week."
That gap between "works" and "fits" is where technical debt accumulates. Fast.
Our framework: Generate, Audit, Integrate
We don't accept AI output as-is. Every piece of generated code goes through three steps:
Generate — Let the AI write it. Don't over-specify. Get the rough shape fast.
Audit — Read every line. Not skim — read. Ask: does this match our patterns? Does it handle the cases we know about? Would we write it this way?
Integrate — Rewrite the parts that don't fit. Rename things to match conventions. Add the error handling the AI missed. Remove the abstractions we don't need yet.
The audit step is where most teams cut corners. It's also where most AI-related bugs originate.
The integration tax is worth paying
Yes, this slows down the initial generation advantage. A function that AI writes in 30 seconds might take 10 minutes to properly integrate.
But that 10 minutes saves hours of debugging later. We've tracked this across eight projects now. Teams that skip integration spend 2-3x more time on bug fixes in later sprints.
What this looks like in practice
When we built Credaro's ledger system, AI generated the initial transaction model and CRUD operations in about an hour. Our audit caught three issues:
- Decimal precision was wrong for currency (floating point instead of integer cents)
- The validation didn't account for our double-entry requirement
- Error messages were generic instead of domain-specific
Fixing these took another two hours. But if we'd shipped the AI version, the decimal bug alone would have caused real financial errors in production.
The meta-lesson
AI doesn't change what good software engineering is. It changes how fast you get to the starting line. The race is still the same: clear thinking, careful testing, honest assessment of edge cases.
Use AI to go fast. Use your brain to go right.