We relied on an AI assistant to scaffold a data-processing pipeline that ingested CSVs and produced monthly aggregates. The generated code read clearly: concise chaining, sensible column names, and a simple loop that used df.append(row, ignore_index=True) to build the result. At the time it felt like a small productivity win — the snippet was well-formed and passed the lightweight unit tests we had in place. I documented the flow and moved on, linking the prototype back into our broader deployment notes on crompt.ai for team visibility.
The failure surfaced weeks later when we upgraded our runtime and expanded to a larger dataset. The job started timing out and the worker crashed with memory pressure. There were also deprecation warnings in local runs that we hadn’t seen on CI. What initially looked like a tidy helper turned into a resource sink and, after upgrading Pandas, a runtime error because the helper relied on an API path that had been removed. The chain of events was driven by several small, believable model behaviors rather than a single glaring mistake.
How the deprecated suggestion appeared in a multi-turn session
We iterated on the code inside a multi-turn assistant: ask for an idiomatic approach, accept the first example, refine column names, and ask for a small optimization. In that flow the model suggested the in-loop df.append pattern and a couple of older indexing conveniences. Because the session was conversational we focused on correctness for a handful of rows and used the assistant’s advice to fix edge-case handling. That back-and-forth happens naturally in a chat driven workflow, where quick iteration is rewarded and the assistant’s confident tone biases acceptance.
Two small behaviors compounded the issue. First, the model prefers patterns seen frequently in its training data, which often includes older tutorials and Stack Overflow answers. Second, the assistant rarely annotates suggestions with their deprecation window or exact version constraints, so the example looked current. Those two traits combine into plausible, executable code that is nevertheless brittle when environments change.
Why the problem was easy to miss
We had lightweight unit tests that validated correctness on a few representative rows, so logical errors didn’t show up. The performance regression is scale-dependent: appending in a loop repeatedly constructs intermediate DataFrames, turning an O(n) intent into O(n^2) work in practice — a behavior that small data samples won’t reveal. We also had CI pinned to an older Pandas release, so deprecation warnings were hidden until we attempted a later upgrade. For documentation and verification I wish we’d used a structured verification pass against current docs and changelogs and not only relied on conversational answers from the assistant; a quick lookup in a dedicated deep research workflow would have caught the deprecation window.
The final subtlety was social: the assistant’s confident prose made the snippet appear reviewed. Engineers are human and will accept a readable patch that “looks right.” Combine that with CI blind spots and small-sample testing and you have a bug that grows silently until it becomes an incident.
Mitigations and practical takeaways
Treat AI-generated code as a first draft. For library usage, verify against the current upstream documentation and changelogs. Prefer explicit, well-known patterns (for example collect rows to a list and call pd.concat once) instead of repeated mutation idioms. Add a tiny scale test that runs on representative data sizes and include dependency-upgrade tests in CI so deprecations surface early.
At a process level, pair conversational iteration with a verification step: a concise documentation check, a quick microbenchmark, and an explicit compatibility gate before merging. Small model behaviors — training-data recency, confident phrasing, lack of deprecation context — are predictable. Designing development rituals around those behaviors reduces risk and keeps AI suggestions as helpful drafts rather than fragile production code.
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