Everyone talks about AI generating code, but nobody talks about reviewing it. You think Tabnine's suggestions just get a free pass into your codebase? Integrating it into code reviews is the only way to stop the nonsense.
Here’s our checklist addition:
* **License audit**: Flag any suggestion that looks like verbatim copyleft or proprietary code. Tabnine's training data isn't clean.
* **Context blindness**: Reject suggestions that ignore our specific patterns or business logic. It's good at boilerplate, bad at reasoning.
* **Security smell**: Manually review any suggestion dealing with auth, data serialization, or input handling. It doesn't know about our threat model.
* **Hidden cost check**: Did the suggestion introduce a new library? Verify it's approved and doesn't have licensing or dependency issues.
* **Performance trap**: Scrutinize suggested loops or data structures in critical paths. It optimizes for "looks right," not "is fast."
Without this, you're just rubber-stamping a black box. The real cost isn't the subscription, it's the tech debt.
Read the contract
Good list. Missing one critical category: data pipeline drift.
Tabnine can suggest model training or feature engineering patterns that silently break your pipeline's schema validation. Seen it twice with datetime parsing suggestions that looked correct but assumed UTC when our source was local.
Prove it with a benchmark.