You know guys, We just finished **75% **of a 6-month project in 2 months!!

All just by AI coding tools!

*Sounds like a win!!? *Wrong damm…

If you see ‘**.csproj’ **file, you see there a lot of deprecated packages. We’ve got the same code copy-pasted across 15 different files. Our payment integration thinks we’re still running .NET Framework 4.0. And our test coverage? Let’s just say it exists… technically.

So this article actually covers our story of how AI made us FEEL like we have powers but while silently turning our codebase to shitt… And more importantly —

how we’re completely rewriting our estimation model, our development process, and our team’s entire approach to AI coding in 2025.

🕸️Let’s talk Setup

So, Client comes to us with an Education Platform Project. And that had just Standard stuff —* student management, content delivery, payment processing, the usual suspects.*

And as our old estimation model if I go vague—

  • CRUD operations — 8 hours per entity
  • Payment integration — 48 hours
  • Third-party APIs — 24–40 hours depending

**Total estimate around **6 months at 44 hours/week

Client was cool with it. No rush. Budget approved. We start vibing.

🕸️But With A.I. (Miracle?)

We know how A.I. works and we were using Cursor, Claude, GitHub Copilot, Windsurf, Code Rabbit — the whole fuckin toolkit.

And damm… HOLY SHIT… We total planned Ten milestones but Seven and a half completed in just 2 Months. invincible right!

“Why did we ever code manually?”

🕸️What the Data Actually Says (We Were Wrong)

Before I tell you what happened to us, let me hit you with some research that’ll blow your mind.

GitHub reported that their Copilot users saw a 26% productivity boost across nearly 5,000 developers at major companies. Sounds great, right?

But you know what! a 2025 study from METR tracked experienced developers working on their OWN codebases. Developers estimated they’d be 20% faster with AI. They FELT 20% faster.

They were actually 19% SLOWER.

not just random number. but there is MATH!

Code Quality—

Research from ***GitClear ***analyzing over 153 million lines of code found:

  • Code duplication increased 4x with AI tools
  • For the first time in history, developers copy-paste more than they refactor
  • Only 30% of AI-suggested code gets accepted — the rest gets tossed
  • 48% of AI-generated code contains security vulnerabilities

And Google?* They’re generating 25% of their code with AI now. But they’re not just letting AI run wild.*

🕸️We reached the limit, and it’s time to understand things

Three weeks ago, we saw that QA team starts filing bugs. Not small bugs. Big, architectural, “how did this even pass code review” bugs.

The payment integration was using packages from 2018. Our user authentication had the same validation logic repeated in seven different controllers. We had unused dependencies bloating our deployment by ~40MB. They pass the test and the reason is very simple, they were testing the wrong things…

We called an emergency meeting…

🕸️Constitutional Convention

We built a system,** A framework**. Rules that would make AI work FOR us instead of against us.

So I did two weeks analyzation— I went full research mode like

  • How do companies like Google actually use AI in production?
  • What strategies are working at scale?
  • Where is AI genuinely making developers faster?
  • Where is it creating technical debt we can’t see?

And I wrote our own constitution. Our AI Development Framework.

🕸️7 Commandments of AI-Assisted Development

1. Human-in-the-Loop, Always

Rule: No AI-generated code reaches production without human review.

I created a mandatory checklist —

  • Architecture alignment
  • Security scan (automated)
  • Logic verification
  • DRY principles check
  • Test coverage minimum 85%
  • Performance impact assessment

One senior dev reviews every PR. No exceptions.

2. Break Everything Into Atoms

Rule: If you can’t describe it in one sentence, it’s too big for AI.

We stopped asking AI to *“build the payment integration.” *Now we ask:

  • “Create the Stripe client initialization”
  • “Add webhook signature validation”
  • “Implement idempotency for payment retries”

One task. One commit. One review cycle.

3. Prompt Engineering is a Core Skill

Rule: Your prompt quality determines your code quality.

And here is the Strategy —

don’t do this — “Create a user authentication system”

instead — “I need a JWT-based authentication middleware for ASP.NET Core 8. Requirements: RS256 signing, 15-minute access tokens, 7-day refresh tokens, token rotation on refresh. Should integrate with our existing UserService. Question: Should we store refresh tokens in Redis or SQL?”

You can clearly view a difference — Context. Constraints. Specific questions that make AI THINK instead of just GENERATE.

4. Test-First, Always

Rule: Write tests before integrating AI code.

We use TDD now. Its important because AI is terrible at edge cases.

process —

  • Write the test cases first
  • Ask AI to implement
  • Run tests
  • Ask AI: “What edge cases am I missing?”
  • Add those tests
  • Refactor

5. Review Code(and it’s MUST)

Rule: AI speeds up writing. Humans speed up SHIPPING.

Every week, we have a 2-hour code quality session. We review AI-generated code from the past sprint. We look for —

  • Patterns that keep repeating
  • Packages we don’t recognize
  • Code that works but feels* “off”*

This is how we learn what AI is good at and where it fails.

6. Measure Everything

Rule: If you can’t measure it, you can’t improve it.

We track:

  • Time saved per task type (CRUD vs complex logic vs integration)
  • Defect rate (AI-assisted vs manual code)
  • Code churn (how often do we have to rewrite AI code?)
  • Team velocity (story points completed per sprint)

7. Redefine Your Estimates

That something I would love to talk with team-leads on email level —

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*Knowing WHEN to use AI (and when not to). Thank You *🖤