We've been actively testing AI-assisted engineering tools at Pre-Approve Me, and what we've learned could save your company from a performance disaster.

When AI Code Looks Perfect—But Isn't

Recently, one of our founding developers built a feature using AI assistance. On the surface, everything worked flawlessly. The code was clean, the feature functioned exactly as designed, and all the tests passed.

Then we looked under the hood.

The AI-generated solution was loading an entire database collection—every single field from every single record—just to extract a simple ID. It then looped through those IDs and fired off additional queries for each one.

The Small Data Illusion

Here's the insidious part: in development, you'd never notice the problem.

With a test database of a few hundred records, the feature runs instantly. Everything feels responsive. The performance metrics look fine. You ship it to production with confidence.

But at scale? That same approach would balloon to:

• Hundreds of thousands of records being loaded into memory
• Hundreds of cascading sub-queries firing sequentially
• Exponential growth in database load and response times
• Skyrocketing infrastructure costs as you desperately scale servers to compensate

This is the performance and cost nightmare that every CTO dreads—and it was hiding in plain sight behind code that "worked."

The Senior Engineering Intervention

With experienced oversight, we caught and refactored the code before it ever saw production:

✓ Query only the fields actually needed
Instead of loading entire records, we now select just the specific fields required for the operation.

✓ Limit results strictly to active context (100-200 records max)
We implemented proper pagination and scoping to ensure we're never processing more data than necessary.

✓ Replace hundreds of sub-queries with a single efficient follow-up
The cascading queries were consolidated into one optimized database call that retrieves everything needed in a single round trip.

The result? The same functionality, but with performance that scales gracefully from 100 users to 100,000.

The Critical Takeaway: AI Amplifies Engineering—Good or Bad

AI code generation tools are remarkable accelerators. They can dramatically speed up feature development and help engineers prototype ideas faster than ever before.

But here's what AI fundamentally cannot understand:

• Scale: AI doesn't know your database will grow from 1,000 to 1,000,000 records
• Production context: Development datasets don't reflect real-world load patterns
• Performance implications: That "working" query might cost you thousands in infrastructure when traffic spikes
• Long-term consequences: Technical debt compounds exponentially when performance issues are baked into core features

AI Doesn't Replace Engineering Judgment—It Amplifies It

Think of AI-assisted development like power tools in construction. A nail gun lets an experienced carpenter work faster and more efficiently. But give that same nail gun to someone without construction knowledge, and they might build something that looks fine but won't survive the first storm.

AI code generation is the same. It will build what you ask for, optimized for your current dataset and current constraints. It doesn't anticipate what happens when:

• Your user base grows 10x
• Your database accumulates years of historical data
• Traffic patterns shift and stress test your assumptions
• Edge cases emerge that weren't in the training data

The Non-Negotiable Rule

If you're using AI to build production software, senior engineers must remain in the loop.

This isn't optional. This isn't about gatekeeping or slowing down innovation. This is about:

• Code review by engineers who understand production scale
• Performance testing against realistic data volumes
• Architecture decisions informed by real-world operational experience
• Risk assessment that AI simply cannot provide

Speed without discipline isn't agility—it's just technical debt accumulating at an accelerated pace.

The Bottom Line

At Pre-Approve Me, we're embracing AI tools because they genuinely accelerate development when used correctly. But we're doing it with our eyes open, with senior engineering oversight on every AI-assisted feature, and with a healthy respect for the risks of treating AI output as production-ready code.

If you know, you know.

And if you don't? If you're shipping AI-generated code without experienced engineering review? You're not building faster—you're just postponing the inevitable performance crisis until the worst possible moment: when you have real customers depending on you.

Don't try selling software built without this discipline. The risk isn't worth it.

What's your experience been with AI-assisted development? Have you caught similar issues before they reached production? Share your story in the comments.

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