We relied on LLM-assisted suggestions to speed up a data cleaning pipeline and, at first glance, everything looked fine. The model produced compact snippets that matched our examples, but several PRs later we started seeing intermittent CI failures and surprising data shape changes in downstream jobs. The root cause turned out to be repeated uses of deprecated Pandas APIs the assistant suggested—APIs that behaved differently across the Pandas versions in our environment. I reproduced and debugged the issue with a mix of local runs and a quick check of historical prompts using crompt.ai to trace which suggestions introduced the risky patterns.
The failure mode isn't dramatic upfront: many deprecated methods continue to work with warnings, or they silently change behavior with subtle performance implications. Because the suggestions read like idiomatic Pandas, they slipped past code review and basic unit tests. It was only when we upgraded a CI image to a newer Pandas that the tests began to fail consistently—by then the suggestions had propagated into multiple modules.
How it surfaced in development
The assistant suggested replacing a loop that accumulated rows with repeated calls to DataFrame.append and a couple of occurrences of df.sort (old alias for sort_values). In our dev environment these snippets passed the unit tests because test fixtures were tiny and used an older Pandas where append still existed. The first clear signal was a CI failure after we bumped the test image to Pandas 2.x: DataFrame.append raised an AttributeError and a fast-moving downstream job silently changed order because of the older df.sort behavior.
We also used multi-turn debugging inside the assistant's chat interface, which reinforced the same pattern—multiple prompts returned variants of the deprecated calls. The assistant's confidence and the consistent formatting made it easy to accept code without checking the exact API docs or the project's pinned dependency constraints.
Why the issue was subtle
Two small behaviors compounded. First, LLMs are trained on a large corpus of public code spanning many library versions; they tend to suggest idioms they've seen often, regardless of current deprecation status. Second, deprecated methods often keep working with warnings or maintain similar signatures for a while, so tests that don't explicitly cover edge cases won't fail. That meant the assistant's advice degraded silently: performance regressed in production and later structural changes caused outright failures.
Another subtlety was naming and context loss. The assistant sometimes changed variable names between examples and suggested helper functions that assumed in-place modification. Those small API and naming mismatches multiplied when code was reused across modules, making the deprecated-call problem harder to isolate.
How small model behaviors compounded into larger problems
The immediate fix—replacing deprecated calls with their modern equivalents (for example, swapping repeated append calls for a single pd.concat)—was straightforward. The harder work was undoing the downstream coupling created by the earlier suggestions: tests that relied on order, performance assumptions, and implicit in-place semantics. What began as a one-line suggestion turned into cross-module churn because the model's small, repeated biases aligned with a common anti-pattern.
Takeaway: treat assistant output as a starting point, not a drop-in replacement for library docs. Small, frequent model tendencies—favoring common but deprecated idioms, renaming inconsistently, or assuming older versions—can quietly spread through a codebase. We found it helpful to add a quick checklist for PRs that touch third-party APIs (check pinned versions, search for deprecations, prefer official docs) and to use visual diffs for complex transformations generated with an AI Image Generator when communicating the changes to non-technical reviewers.
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