AI-generated code often looks clean and production-ready but hides subtle context-related flaws that slip past standard code review. The core problem is that AI models fill in missing context with generic patterns that may not fit the specific system, leading to bugs that surface late and are costly to fix. The solution isn't just better review — it's better prompting. Teams should treat prompting as an iterative, human-in-the-loop process, embedding system context upfront and establishing shared prompting standards. This shifts validation earlier in the workflow, reducing downstream debugging costs while still capturing AI's speed benefits.
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