Productionizing AI-Generated Scrapers: Adding Monitoring, Logging, and Alerts to Playwright Scripts

AI tools like ChatGPT and GitHub Copilot have fundamentally changed the speed at which we can build web scrapers. We can now generate complex Playwright scripts with accurate CSS selectors and extraction logic in seconds. However, a significant gap exists between a script that runs on your laptop and one that survives in a production environment. AI is excellent at solving the "extraction problem" the what of scraping. But it often fails at the "operational problem" the how of maintaining reliability over time. When a website changes its layout or a proxy fails, a raw AI-generated script often fails silently, returning empty data while reporting a successful run. This guide takes a raw AI-generated script from the Dermstore.com-Scrapers repository and transforms it into a reliable, production-ready data pipeline. Prerequisites To follow along, you should have: Intermediate Python knowledge (decorators, dataclasses, and logging). Playwright installed ( pip install playwright ). A basic