37 minutes to fix your broken AI code

Claude Code was producing garbage until I wrote 70 tests covering every edge case.

Everyone's so obsessed with shipping faster with AI, they've forgotten how to ship software that works.

We've traded craft for velocity. Tests for speed. Quality for "good enough."

Here's what 10 years building AI taught me: AI makes the fundamentals MORE important, not less.

The difference between AI slop and code you can trust? The boring stuff we all stopped doing. Tests. Error handling. Architecture.

I learned this the hard way—when my "move fast" RAG system wasn't actually reading in any context documents and was confidently acting like it did.

Now I write 70 tests before shipping. Slower? Honestly, I don't think so. In the long run it speeds things up significantly, and I sleep at night.

When My AI Brain Started Making Things Up

I'm building my own AI "brain"—a RAG system that has access to all my documents, notes, and knowledge. The goal: an AI assistant that actually knows my context, my projects, my style.

Three weeks in, I asked it about my previous startup experience.

It gave me a detailed answer. Specific dates. Project names. Even quotes from documents.

One problem: It had never actually loaded my Google Drive files.

The document loader was silently failing, returning empty arrays, and the pipeline just... continued. No errors. No warnings. The AI was confidently hallucinating my entire professional history.

That's when it hit me: I had no idea if ANY part of my system was actually working.

The Trust Problem Nobody Talks About

When you use Claude Code or ChatGPT to build your system, you get code that looks right. It runs. It returns responses.

But here's what I couldn't answer:

• Were documents actually being chunked correctly?

• Were embeddings actually being stored?

• Was retrieval actually using semantic search or just returning random documents?

• Would the system survive a restart?

Without tests, AI-generated code is a black box inside a black box.

You're not just trusting the AI model's outputs. You're trusting code you didn't write to correctly handle data you can't see through processes you don't understand.

Why Testing AI Is Completely Different

Traditional code either works or it doesn't. Your database query returns results or throws an error. You know immediately.

AI systems? They fail gracefully. Too gracefully.

Your document loader fails silently. The pipeline continues with empty data. The LLM generates plausible-sounding nonsense. Everything looks fine until a customer catches the hallucination.

Here's what I learned the hard way:

What You'll Learn Inside (Paid Subscribers Only)

The 3-Layer Testing Framework that catches 90% of AI system failures before production—including the silent failures that only surface at 3 AM

The 7 Pre-Commit Hooks that have caught 3 leaked API keys, 17 type mismatches, and 43 logic bugs before they reached production—takes 5 minutes to set up, saves hundreds of hours

The TDD Approach for AI that makes you ship faster, not slower—including the exact 3-step process to trust AI-generated improvements in 30 seconds instead of 3 hours of manual testing