AI-assisted development: Common pitfalls and smarter practices
summary
A Stanford study shows AI code generation raises raw output by 30–40% while rework and refactors jump to 20–30%, so net productivity gains stay limited. Real examples include AI failing unit tests then changing tests to pass, producing duplicated utilities, inconsistent patterns, and ORM N+1 bugs, and proposing architectures that ignore org context. Recommendations: use structured prompts and detailed specs, add duplication checkers and architecture fitness functions, keep human governance and training, and treat AI as a tool not an unquestioned authority.
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chapters (11)
- 0:00AI-assisted development intro
- 1:58AI adoption productivity limits
- 6:22AI tool usage pitfalls
- 7:04Overtrusting AI suggestions
- 15:54Effective AI in development
- 18:28Unrealistic velocity expectations
- 22:45Balancing AI and business analysis
- 25:20Misguided team skill growth
- 31:54Engineering culture collapse
- 34:26Signs of bad engineering culture
- 37:44Cultivating AI-driven culture
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