Always Validate AI Suggestions Before Merging
AI-generated code often looks plausible but contains subtle logic errors, security vulnerabilities, or performance bottlenecks.5 Blindly trusting and merging this code is dangerous, erodes quality, and creates massive, hidden technical debt.
You must treat all AI-generated code as untrusted until proven otherwise. Adopt a "trust but verify" mindset and implement a formal validation workflow for all AI-assisted code before it is merged into the main branch.
This is the most critical human-in-the-loop guardrail. AI models are optimized to satisfy the prompt, not to adhere to your team's unstated risk model or business context.5 This core misalignment is the source of: pain-point-01-almost-correct-code: Subtle logic errors that look correct, like using == "admin" instead of checking a roles array, leading to an authorization bypass.5 pain-point-19-insecure-code: Critical "omissions of necessary security controls," where the AI forgets to add input validation, sanitization, or error handling because it wasn't explicitly asked to.5 Thinking of the AI as a "brilliant but potentially drunk/high on adderall coworker" is the correct mental model. The developer who hits "merge" is 100% accountable for the code, regardless of its origin. A "trust but verify" process, where the developer rigorously audits and tests the code, is the only way to prevent security and quality failures and solve the long-term pain-point-02-trust-deficit.
This is a universal, mandatory practice for every developer using any AI coding assistant, on every piece of generated code, no matter how small.
The "verify" step must be a structured process, not just a quick glance. Use a formal verification checklist for all AI-generated code: Validate Functional Correctness: Does it actually work? Write and run tests to prove it. The TDD workflow (Rec 7) is the best way to do this. Review for Code Quality: Is it readable, maintainable, and does it follow your team's style guides? Check Security: Explicitly look for what's missing. Is input validated? Are errors handled? Are there any hardcoded secrets? Inspect Performance: Does this introduce a bottleneck? Is it using resources efficiently? Apply Static Analysis: Run linters and static analysis tools on the code to catch what the human eye missed. The human developer's role is to provide the context, domain expertise, and ethical judgment that the AI lacks.
Workflows that implement or support this recommendation.
- Understanding Security Risks in AI-Generated Code | CSA - https://cloudsecurityalliance.org/blog/2025/07/09/understanding-security-risks-in-ai-generated-code
AI models are optimized to satisfy the prompt, not to adhere to your team's unstated risk model or business context.
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Engineering Leader & AI Guardrails Leader. Creator of Engify.ai, helping teams operationalize AI through structured workflows and guardrails based on real production incidents.