When implementing AI model components like tokenizers, favor simplicity over rarely-used features that significantly increase code complexity. This is especially important for performance-critical paths in machine learning pipelines. Consider removing or deferring implementation of features that: 1. Require complex argument parsing 2. Are used only in specialized cases 3. Introduce significant maintenance burden Example: ```typescript // AVOID: Complex implementation with rarely-used features let encodeBatch = promisify(tokenizer.encodeBatch.bind(tokenizer)); var output = await encodeBatch( [["Hello, y'all!", "How are you 😁 ?"], ["Hello to you too!", "I'm fine, thank you!"]] ); // BETTER: Simplified implementation focusing on core functionality var output = await tokenizer.encodeBatch(["Hello, y'all!", "How are you 😁 ?"]); ``` This approach helps maintain performance in AI inference paths while keeping the codebase maintainable. Features can always be added later when there's a clear need and sufficient time for proper implementation.