I used to think I was good at debugging.
I knew my stack. I wrote clean code. I added logs like a paranoid squirrel.
But last month, I hit a wall—one of those bugs where nothing makes sense and every Stack Overflow thread ends in silence.
What fixed it wasn’t a search.
Wasn’t a linter.
Wasn’t my senior engineer friend.
It was an AI that understood my architecture, runtime, and logic chain better than I could hold in my head.
Here’s what happened—and why I now treat debugging like a conversation, not a puzzle.
The Bug That Didn’t Want to Die
The setup:
Stack: Node.js backend (Express + Mongoose), React frontend, Stripe integration
Issue: New users could register. Email verification worked. But on paid plans, tokens expired prematurely, and Stripe webhooks weren’t syncing payment status.
Symptoms: No clear error. Random 401s. Broken user session flow. Logs lied.
I had three theories:
Middleware bug
Token TTL mismatch between DB and client cache
Stripe event timestamp issue due to timezone drift
Each felt plausible.
Each burned hours.
None panned out.
Bringing in AI: Not for Answers—For Systems Thinking
I dropped a simplified version of my repo into Document Summarizer, asking:
“Here’s my app. Help me locate logic inconsistencies between auth flow, payment update, and session management.”
Instead of throwing back generic advice, the AI:
Mapped my route dependencies
Noted that verifyUser() was async—but not awaited in handleStripeWebhook()
Flagged that updateUserPlan() was conditionally skipping Stripe sync if req.user.plan === ‘free’—even though session state hadn't caught up
It caught state mismatch at runtime caused by async side-effects across two services.
Something I never noticed—because the bug lived between files, not in them.
That’s when it clicked:
Debugging isn’t about finding broken lines. It’s about tracing broken systems.
And AI sees systems better than I do.
How It Actually Works (Under the Hood)
Here’s the workflow I’ve now repeated across projects:
Step 1: Ingest the Code
Feed core modules (controllers, middleware, services) into the Document Summarizer. This builds a mental model of function flow, data shape, and module dependencies.
Step 2: Ask Logic-Based Questions
Instead of “why is X breaking,” I ask things like:
“Which functions modify session state and which async calls affect auth flow?”
These questions are best run through Socratic AI, which pushes back, clarifies intent, and probes edge cases you haven’t considered.
Step 3: Compare Assumptions Across Files
I ran authController.js vs stripeService.js through Compare Anything with this prompt:
“Find where assumptions about user plan diverge between modules.”
Result?
Stripe assumed user.plan updated after DB write.
Frontend assumed it changed after redirect.
Classic async race condition. Unseen until the AI compared architectural expectations.
Step 4: Improve Output Clarity
Once logic was patched, I used Improve Text to rewrite error messages, comments, and PR descriptions—making future debugging clearer for the team.
This turned my spaghetti commit into something teachable.
Why Static Tools Couldn’t Catch It
Linters don’t understand runtime context.
Type checkers can’t see async behavior across microservices.
Logs only help if you already know where to look.
But this AI stack caught the why—not just the where—by doing something no debugger does:
It mirrored my thinking, not just my syntax.
It understood my intentions, my architecture, my stack-specific quirks.
Not because it was magic. But because it had context.
And in modern dev workflows, that’s everything.
What Changed After This
Since then, my entire approach has shifted.
I debug with dialogue, not monologue
I pair natural language prompts with side-by-side file analysis
I treat my codebase like a living document, not a frozen artifact
I’ve used this system to catch early-stage regressions, validate refactors, and onboard junior devs faster—by letting the AI explain how the code thinks, not just how it runs.
-Leena:)
Top comments (0)