A practical maintenance schedule might review your top-performing content quarterly, your mid-tier content semi-annually, and your long-tail content annually. During these reviews, you update statistics and examples, add new sections covering recent developments, remove or update outdated information, and add a new "last updated" date to signal freshness. This regular maintenance keeps your content competitive and shows both AI models and human visitors that you're actively maintaining accuracy.
Trained — weights learned from data by any training algorithm (SGD, Adam, evolutionary search, etc.). The algorithm must be generic — it should work with any model and dataset, not just this specific problem. This encourages creative ideas around data format, tokenization, curriculum learning, and architecture search.
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