Review for AI-generated code
What review must establish when an assistant produced some or all of a change. The submitter understands it and can explain it; it is tested at the level the risk warrants; its origin is disclosed where required; it is checked against the vulnerability classes that generated code is prone to; and it imports no unacceptable licence. The standard is set before adoption and is proportionate to the change's risk.
Evidence grade: practice A widely adopted practitioner model with clear provenance.
Use it when
Every team using AI assistance, enforced in the pipeline where it can be.
Where it stops working
A review standard that tries to keep pace with volume stops establishing understanding. This one holds review to understanding and lets throughput be whatever it turns out to be.
Where the standard uses it
- TJ-3Code review and quality standardsIncluding standards for AI-generated code.
Used alongside it
- Aligned autonomy practice
- Code review as knowledge transfer research
- Contract at the boundary practice
- Quality as a system practice
- Security of AI-generated code research
Cite this model
Chicago, BibTeX and licence →Engineering Management Academy. (2026). Review for AI-generated code. In Engineering Management Academy Body of Knowledge (Version 3.1, TEC.review-for-ai-generated-code). https://www.engineeringmanagement.academy/body-of-knowledge/techniques/review-for-ai-generated-code