Implement token-efficient patterns when working with AI models to optimize costs and performance. Key practices include: 1. Batch identical prompts into single API calls where supported 2. Leverage provider-specific features for token efficiency 3. Use appropriate token counting methods for accurate estimation Example of efficient batching with OpenAI: ```typescript // Inefficient: Multiple separate calls const results = await Promise.all( inputs.map(input => model.generate(input)) ); // Efficient: Single batched call const results = await model.generate( [promptValue], { n: inputs.length } // OpenAI charges input tokens only once ); // For accurate token counting: const tokenCount = await model.getNumTokensFromMessages(messages); ``` This approach can significantly reduce costs, especially for use cases with high input token counts relative to output. Different providers may offer varying batching capabilities - consult their documentation for optimal usage patterns.