How do you ensure the quality and safety of AI-generated code?
Q: How do you ensure the quality and safety of AI-generated code?
The fundamental principle is simple but critical: understand every single line of code that enters your codebase, regardless of whether it was written by a human or an AI. This isn't optional — it's a core responsibility of software engineering. AI can produce code that runs perfectly but contains severe logical flaws, such as a payment gateway integration that inadvertently deletes all customer data. The code might be syntactically correct and pass initial tests, yet harbor devastating business logic errors.
Key Validation Techniques
To mitigate these risks, employ multiple layers of verification:
1. Request Complete, Minimal Working Examples
Always ask the AI for fully executable, minimal code samples rather than snippets. This serves as an immediate hallucination detector — if the AI has invented functions, methods, or parameters that don't exist, the code won't run. This simple technique quickly exposes when an AI has confidently generated plausible-looking but non-existent APIs.
2. Demand Documentation References
Request explicit references to official documentation for any libraries, frameworks, or APIs used. This forces the AI to ground its suggestions in real, verifiable sources and reduces the likelihood of hallucinated functionality. When the AI can't provide legitimate references, treat the code with extra skepticism.
3. Generate Tests for Validation
Use AI as a testing partner — have it generate comprehensive test cases for the code it produces. This is particularly valuable for complex or hard-to-test functionalities like server-sent events, WebSocket connections, or asynchronous operations. The process of creating tests often reveals edge cases and potential failure modes that weren't immediately obvious in the implementation.
The Human Review Imperative
These techniques aren't substitutes for human judgment — they're tools to enhance it. Every piece of AI-generated code must undergo the same rigorous review process as human-written code. The difference is that with AI-generated code, you need to be especially vigilant about:
Logical correctness beyond syntactic validity
Business rule violations that might not trigger errors
Security implications that emerge from subtle implementation choices
Performance characteristics under real-world conditions
Remember: AI excels at producing code that looks right and often runs correctly in simple cases. The danger lies in the gap between "runs without errors" and "does what the business actually needs safely and efficiently."
For a more in-depth discussion, see this talk with Danny Roy Greenfeld and Isaac.