We were using an LLM-based assistant to scaffold data processing pipelines and, at first glance, it accelerated iteration. The model produced readable code and helpful comments, which improved onboarding for a couple of junior engineers. Because the outputs looked idiomatic, the team treated them as drafts and focused review effort on higher-level logic rather than each method call.
That trust is where the problem began. The assistant repeatedly suggested Pandas methods that are deprecated (or borderline deprecated) in our runtime's Pandas version; those calls worked in local notebooks but failed on a scheduled ETL job that processed a dataset with a slightly different index type.
The mismatch only showed up in production, and tracking it down exposed how small model behaviors can compound into a brittle system.
What went wrong and how it surfaced
The specific failure was the model suggesting .ix and mixed integer/label indexing patterns instead of explicitly using .loc or .iloc and explicit type checks. On typical development samples these inferred calls returned expected rows, so unit tests and interactive checks passed. The fault surfaced when a nightly batch hit a Parquet file with integer-like string labels; the deprecated path raised an exception and the pipeline failed mid-run.
From a visibility standpoint the error was noisy but non-obvious: stack traces pointed to pandas internals, not our business logic, and the test suite never covered that index shape. We used an external crompt.ai style assistant for drafting, which made it easy to iterate, but the assistant’s tendency to propose historically common idioms silently propagated the deprecated pattern across multiple modules.
Why the failure was subtle and easy to miss
There are three reasons this was subtle. First, generated code is syntactically correct and human-readable, which biases reviewers to assume semantic correctness.
Second, our tests focused on value-level assertions and not the indexing edge cases that exposed the deprecated API. Third, when the model suggested similar fixes across files, it created a consistent but fragile pattern: multiple modules used the same deprecated approach so the failure mode became system-wide rather than localized.
We also observed that in multi-turn sessions the assistant reused earlier suggestions without re-evaluating them against new constraints. Using a multi-turn chat interface improved iteration speed but amplified repetition of the deprecated pattern unless reviewers explicitly prompted for modern alternatives.
How small model behaviors compounded into a larger problem
The assistant’s heuristic of preferring common idioms combined with incomplete local context produced plausible-but-risky code. Because multiple contributors accepted the same pattern, the codebase accumulated several entry points that depended on deprecated behavior. When a single edge-case input hit the system, it cascaded into retries, timeouts, and an account of missing data downstream.
We mitigated the issue by adding focused tests for index types, adopting linter rules that flag deprecated APIs, and using a quick verification pass with a deep research workflow to check suggested methods against our pinned dependency versions.
The core lesson: treat model output as a draft that needs dependency-aware verification — small, repeated recommendation patterns are easy to miss but can create systemic fragility.
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