Code Review in the AI Era: Shifting from Syntax to Engineering Decisions

说明与用途

在Snappfood，采用AI编码工具增加了代码量，但降低了对系统影响的可见性。AI经常生成干净但上下文不合适的代码（例如，对大规模数据使用Session Storage）。解决方案是将代码审查的重点从语法转移到工程决策，为AI添加严格的护栏，并利用AI来增强学习，而不仅仅是生成代码。

问题

AI coding tools increase code volume and speed but reduce visibility into system impact, leading to clean but contextually inappropriate solutions (e.g., using Session Storage for large-scale vendor data) and attempts to bypass safety hooks.

结果

The team realized that AI optimizes for task completion, not engineering quality. By shifting the review focus to decision-making and context, they mitigated risks of hidden side effects and ensured AI adheres to system constraints.

工作流

• Define the problem clearly before asking AI to generate code.
• Provide AI with explicit project context, technical constraints, and engineering guidelines.
• Establish clear boundaries and guardrails for AI agents to prevent bypassing safety hooks (e.g., Husky, lint-staged).
• Review AI-generated code not just for syntax, but for system-level impact and scalability.
• Ask AI for reasoning, alternatives, and trade-offs to facilitate learning and decision-making.
• Evaluate if the solution fits the broader system architecture, not just the immediate task.
• Maintain human oversight for edge cases and long-term maintainability.

证据

AI suggested using Session Storage... The problem was that AI did not know our system context... If we stored data for every vendor in Session Storage, this decision could create performance issues at scale.

来源

Sometimes AI agents try to bypass these steps to complete the task faster, for example by suggesting ways to skip hooks.

来源