It actually works. I have over 100 creators and businesses actively using the platform, with real students enrolling and real money flowing through the system. But as the user base grows, the "magic" is starting to feel a lot more like a ticking time bomb.
There is a massive, terrifying gap between "it works" and "it is well-engineered." While my AI-driven development workflow allowed for rapid deployment, I am acutely aware that I am essentially a non-engineer managing a complex codebase I don't fully comprehend. I'm not looking for a pat on the back; I'm looking for a reality check on how much technical debt I've actually accumulated.
If a senior engineer were to perform a high-stakes audit on a non-coder's AI-generated stack right now, where should I be looking to prevent a total system collapse? I’ve been focusing on features, but I realize I might be ignoring the structural integrity of the entire application.
The critical areas I'm worried about #
Since I am essentially "guilty until proven innocent" in the eyes of the engineering community, I need to prioritize my deep dive into the following domains:
Data Isolation and Multi-tenancy: This is my biggest fear. In a course platform, if Creator A can somehow access Creator B's student data or private course content due to a flawed query generated by an AI, the business is dead instantly. I need to ensure that my database architecture enforces strict isolation at the row level or through robust middleware.Authentication and Authorization Logic: I've used AI to implement login flows, but logic flaws in permission handling are common. I need to verify that "student" roles cannot escalate privileges to "admin" or "instructor" via manipulated API requests.Payment Integrity and Webhooks: Handling money requires more than just a working Stripe integration. I need to ensure that my backend correctly validates webhooks and handles edge cases like failed payments or subscription cancellations without leaving the system in an inconsistent state.AI-Generated Code Quality and Security: LLMs are notorious for suggesting code that is functional but insecure (e.g., vulnerable to SQL injection or XSS). I need a way to audit the patterns the AI is repeatedly using.Scalability and Performance: As the number of concurrent students grows, will the architecture hold up, or will the unoptimized queries I've blindly accepted cause a massive bottleneck?
I'm essentially trying to transition from an AI-driven prototyping phase to a legitimate production-grade deployment. I need to move beyond just "making it work" and start implementing a real AI workflow that includes automated testing, linting, and security scanning to catch the mistakes my prompts might be introducing.
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