The Pydantic example hits different because instead of scattering data cleaning logic all over your extraction code like digital breadcrumbs, you're centralizing it in validators where you can actually test it in isolation, which is chef's kiss. But even with Pydantic watching your back, silent failures can still slip through if you're marking fields as Optional with default values, so for anything running in production, I'd really push for making your scraped fields mandatory and throwing in those gt=0 constraints on numbers to catch those sneaky zero-price bugs that usually mean your selectors have ghosted you. The fallback selector strategy is lowkey underrated and deserves way more shine - the real move isn't just having backup selectors sitting around, it's actually monitoring which tier ends up succeeding, throw some Prometheus metrics on it, so you catch drift before your validation errors explode. And one more thing, only chase that AI-assisted repair route if it actually makes financial sense, which means you gotta test those LLM-suggested selectors against your actual captured HTML in a sandbox before you go shipping them - otherwise you're basically deploying the model's hallucinations into production and making things way worse