Summary: The Pitfalls of AI in Code Generation
Yesterday, I highlighted the limitations of AI-generated code as “plausible-looking, locally coherent, globally wrong.” Here’s a personal example that underscores this critical issue.
In my Slack-sup2 app, I initiated a cleanup job for old group DMs. The AI’s initial solution seemed flawless, yet it caused significant disruption:
- Rate Limit Oversight: Slack’s API permits only one request per second. Running this job against multiple conversations led to a complete system failure.
- Worsening the Problem: The AI’s suggestion for error handling only exacerbated the situation, blocking essential operations.
The Real Fix:
- I implemented a solution utilizing cron jobs to manage requests effectively.
- Added features to auto-close a limited number of DMs, ensuring the system remains operational.
This experience vividly illustrates why human oversight in AI-generated solutions is non-negotiable.
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