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When AI Writes Bugs: Field Notes from Real Cleanups
AI & Craft

When AI Writes Bugs: Field Notes from Real Cleanups

Precision Build
10 min read
Patterns of failure in AI-generated code and how senior devs fix them.
#code-quality#security#performance#ai-copilot
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Common issues in AI-generated code include missing invariants, unchecked input, and silent performance footguns like N+1 queries. Senior engineers triage by adding failing tests that capture real-world scenarios, then refactor to reveal domain boundaries. Security reviews prioritize authz gaps, dependency trust, and supply-chain hygiene. Success requires discipline: human review, static analysis, and runtime telemetry to validate behavior under load. AI can accelerate scaffolding, but craftsmanship—naming, contracts, and observability—is still human. The best teams pair generation with review checklists and a CI gate that enforces quality.

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Article Info

Category:AI & Craft
Read time:10 minutes
Author:Precision Build
Published:Oct 2025

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