When AI Writes Code That Doesn’t Exist: How to Sanity-Check AI-Suggested Implementations

September 19, 2025 at 08:17 AM

Søren Beck Jensen Founder, AppStuck

300+ AI app rescues across Lovable, Bolt.new, Replit, Cursor & Windsurf

Ever asked an AI assistant to implement a feature and watched it confidently return a solution, only to find out the method or library it used doesn’t even exist? You’re not alone. Here’s how to stay sane while working with AI-generated code.

When AI Writes Code That Doesn’t Exist: How to Sanity-Check AI-Suggested Implementations

For those of us building apps with no-code and AI tools, it’s become increasingly common to ask LLMs (like GPT-4 or Claude) to generate code, refactor components, or debug issues. But as powerful as these tools are, there’s a dark comedy to how often they just... make things up.

Imagine asking your AI co-pilot to help integrate a new auth library, only to get back code using a .Fantamize() method that doesn’t actually exist in any documentation. It's frustrating, time-wasting, and worst of all, it often feels like you're being gaslit by your AI.

Why This Happens

AI models like GPT-4, Claude, and Codex are trained via next-token prediction, not firm knowledge of coding standards or verified documentation. That means they frequently imagine plausible-sounding classes, methods, or APIs based on patterns they’ve seen before. Some methods exist. Some don’t.

Sometimes they’re a version or two off from current docs. Worst-case: they hallucinate whole libraries, implement features using deprecated syntax, or reference broken URLs that never existed.

Common “AI Lies” To Watch For

Imaginary Methods: Functions that sound perfect but aren’t part of any library.
Fictional Libraries: Libraries that were never released or don't exist on npm, PyPi, etc.
Erroneous Refactors: Suggested code changes that silently break functionality.
False Positives on Fixes: AI says “the issue is fixed”, but nothing actually changed.

How to Sanity-Check AI Code

Google It: Before you copy-paste anything, search the method or error. If no Stack Overflow posts, GitHub issues, or official docs mention it, suspect a hallucination.
Docs Or It Didn’t Happen: Ask the AI to include links to official documentation. If it provides a URL, click it. If the page 404s, call it out.
Pair With Simpler Tools: Use rule-based tools (like linters, pre-commit checkers, or no-code validators) to flag impossible completions early.
Version Check Everything: Sometimes a suggested method does exist, but only in an unreleased beta version. Always confirm compatibility with your stack version.
Build AI Memory Into Your Dev Workflow: Tools like AGENTS.MD (or Workspace Rules in Windsurf) help cache your setup and constraints so the AI can respond more contextually, and less imaginatively.

How to Use AI Effectively Anyway

Despite these headaches, AI assistants are still incredibly powerful, especially when you treat them as interns, not senior engineers. They’re brilliant idea generators and drafting tools, but reliant on your oversight.

Use them to:

Rapidly scaffold rough UI or backend logic
Iterate on design patterns or architecture
Translate between languages/frameworks
Debug isolated, well-scoped issues

Just don’t blindly trust their implementations. You wouldn’t rubber stamp code from a junior dev without reading it, AI deserves the same caution.

Final Thoughts

Using AI and no-code platforms to build apps is like having a high-speed bike, without brakes. You can go fast, but you need to learn when to stop, validate, and steer with care.

AI might suggest .Fantamize() all day long. Just make sure that function isn’t a figment of its neural imagination before you deploy.