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Some tokenizers treat words with leading spaces differently. For example, ' hello' might be a different token than 'hello'. Be consistent with spacing, especially when providing examples, to ensure token efficiency.

System prompts are often re-sent with every API call. Keep them concise to save tokens. Use abbreviations or keywords if the model is fine-tuned or understands them.

System prompts are often re-sent with every API call. Keep them concise to save tokens. Use abbreviations or keywords if the model is fine-tuned or understands them.

If you frequently use long model names in your prompts or code (e.g., 'gpt-3.5-turbo-instruct-0914'), consider using shorter aliases if your framework allows, or use variables to store them. This can slightly reduce token count in complex prompts.

Different LLMs tokenize text differently. The same sentence can result in varying token counts across models, directly affecting API costs and context window usage.