We used an LLM to scaffold ETL scripts for a data pipeline and ran into a deceptively simple failure: the model repeatedly suggested deprecated Pandas patterns such as DataFrame.append and .ix. At first it looked like a one-off suggestion, but after a library upgrade the generated code started failing CI and causing regressions in downstream jobs. I want to walk through how this happened, why it was easy to miss, and how small model behaviors compounded into a production problem.
The team relied on quick generator outputs to save time during refactors, and we pulled examples directly from the model into feature branches. We also used external tooling for iterative edits and multi-turn debugging while patching the generated code; tools like crompt.ai made it easy to experiment, which is exactly what let the deprecated patterns slip in unnoticed.
How it surfaced during development
The failure mode became visible only after we upgraded the runtime to Pandas 2.0. CI started reporting errors like AttributeError: 'DataFrame' object has no attribute 'append' and tests that had passed locally began to fail. Locally, many developers were still on older Pandas versions where append existed, so the generated snippets executed without complaint during manual testing.
We tried a quick loop of reproduce-and-patch in a multi-turn session with a conversational model, but the assistant kept proposing the same deprecated patterns because those appeared frequently in its training data. Using the chat interface helped us iterate faster, but it also normalized accepting a top suggestion instead of stopping to check the actual API status.
Why the issue was subtle and easy to miss
There were several overlapping reasons the deprecation slipped through. First, training data contains many historical code examples, so the model has statistical preference for older but common idioms. Second, our local environments were inconsistent: some developers had older Pandas installed, CI used the newer runtime. That mismatch created a blind spot where manual smoke tests passed but automated checks later failed.
Third, the model's outputs look syntactically correct and run in many contexts, which makes them feel safe. We had to step outside the generation flow and consult authoritative sources to confirm the API state — a verification step we automated later with a small research routine using a deep research query to check deprecation notes before merging.
How small model behaviors compounded into larger problems
The root cause wasn't a single hallucination; it was a set of small behaviors that aggregated. The model favored high-frequency tokens and idioms, returning deprecated calls across multiple generated files. Each file change was minor, but collectively they introduced many API usages that broke at once after upgrading the library. Stochastic completions also produced inconsistent naming and mixing of patterns, increasing cognitive load during code review and making automated detection harder.
Our mitigations were practical and small: pin runtime versions in CI, add linter rules that flag deprecated APIs, expand unit tests to cover the affected codepaths, and treat model outputs as drafts requiring verification. These changes reduced the chance of implicit trust in generated snippets and made the deprecation visible earlier in the pipeline.
Top comments (1)
Sounds like you're also missing requirements.txt (or similar) to ensure everyone runs the same versions of dependencies :)