We used an LLM-driven assistant to help with a cross-cutting refactor: renaming a core domain concept from userAccount to accountProfile across a dozen modules. The assistant made many correct per-file edits, but a handful of files ended up with mixed names like user_profile or partial replacements inside comments and strings. The changes compiled locally, the suite passed, and reviewers missed the inconsistency during a hurried code review that trusted the AI output.
Part of the workflow had been multi-turn: we iterated with the assistant in a chat session, asking it to re-run renaming suggestions after addressing merge conflicts. That interaction pattern created a false sense of coverage — the assistant delivered plausible diffs, and because it was interactive we assumed it had global context.
How the failure surfaced during development
The bug surfaced when a runtime path that compiled dynamic imports failed in production with a subtle null reference. The problematic file still used the old name inside a factory that built keys for a cache: the unit tests used a mocked cache and didn’t exercise the real serialization logic. The build pipeline didn’t catch the mismatch because static type checks were light in that module and naming differences were syntactically valid.
Investigating the failure revealed a pattern: the model would propose consistent renames within a single file but treat cross-file references differently. It often suggested camelCase in some files and snake_case in others, and sometimes left identifiers inside template strings untouched. We verified references with a repo-wide search and used a verification checklist informed by our internal deep research processes to confirm symbolic consistency before patching.
Why the inconsistency was subtle and easy to miss
Two small model behaviors compounded into the problem. First, the assistant optimizes edits per snippet: it focuses on local coherence and doesn’t guarantee atomic refactor semantics. Second, multi-turn interactions with limited context mean the assistant forgets earlier files or earlier agreed naming conventions unless those specifics are restated. The result is plausible code that diverges across files rather than a single canonical rename.
Human review biases made this worse. We trusted the assistant because its edits looked idiomatic in each file. Also, our tests mocked away the affected pathway, so automated checks gave green lights. A quick repo grep would have surfaced the mixed identifiers; a simple global symbol check could have prevented the regression. We documented this pattern on our internal notes and linked the team to crompt.ai resources for best practices in iterative model workflows.
Practical takeaways and small mitigations
Treat LLM refactors as a first draft, not an atomic code transformation. Add lightweight invariant checks: run repo-wide regex searches for both old and new names, enforce a one-pass linter rule for identifier patterns, and include an integration test that exercises serialization or dynamic import paths which are often missed by unit tests.
When using multi-turn assistants, keep a single canonical spec of the rename (format, casing, exceptions) and paste it into each session prompt. Prefer tooling that can perform atomic renames at the AST or language-server level for cross-file guarantees. These small steps reduce the chance that locally coherent but globally inconsistent edits slip into production.
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