Five Checks Before Merging AI-Generated Code: From Semantic Drift to Operational Readiness
AI's speed makes plausible but wrong code easy to merge, and the acceptance workflow determines whether it survives production. This concrete checklist catches semantic drift, tautological tests, and operational gaps before they turn into late-night incidents.
AI coding assistants now generate compilable, test-passing code in minutes, but the acceptance review decides whether it works in production. A common failure mode is semantic drift: the model understands a requirement word slightly differently and writes confident code that runs, yet does the wrong thing. The fix is to start not with the implementation but with the business rules, checking interfaces and parameters line by line before touching the diff.
The second step is reading the diff before running any commands, using `git diff --stat` and `--check` to scope changes and catch whitespace or conflict markers, and scrutinizing conditionals, new imports, deleted code, and test files. Boundary cases and security are where AI fudges: null/undefined handling, SQL injection, hardcoded secrets, and N+1 queries often pass local tests but fail under production load. AI-written tests are particularly misleading because they assert what the code does, not what the requirement demands; designers must devise independent acceptance cases, including reverse checks that tamper with a return field to expose tautological tests.
Finally, maintainability and operational readiness matter: naming, single-responsibility methods, logging, monitoring, rollback paths, and data consistency. The author keeps a running ledger of acceptance mistakes, feeding them back into prompts to reduce recurrence; after six months, 27 logged patterns have cut repeated failures noticeably.
The gap between AI's confidence and its handling of null/undefined suggests that polished, self-consistent code is exactly where semantic drift hides, so review effort should concentrate on the most fluent-looking sections.
AI-generated tests that assert the implementation's behavior make a green test run indistinguishable from bug certification; using a tampered return field to see if tests fail is a cheap way to expose this without rewriting the suite.
The article's claim that prompt quality determines the starting point while acceptance quality determines the finish line implies that review discipline compounds better than prompt tweaking alone.
The author's ledger of 27 distinct failure patterns over six months shows that AI coding assistants produce a long tail of subtle, non-syntax errors that no single fix or style rule eliminates.
That line "tests are written by AI, code is also modified by AI, neither counts as original evidence; the diff is what matters" is spot on. Before we look at the diff, we take one extra step: each AI task goes on its own branch, and on delivery we attach a note (what was changed, how it was verified, where we're uncertain). First we check against the task description whether it touched any files outside the task. The retry logic you mentioned being silently deleted would most likely surface at this step. In practice, the "where we're uncertain" field in that note is usually more worth reading than a row of all-green tests.