According to Decrypt, AI companies charge users by the token for model usage. Many individuals and teams expend tokens on tasks that could be completed by less expensive or simpler models. The article outlines practical approaches to reduce token consumption while achieving stronger results, beginning with prompt design and extending through early agent development.
It explains that selecting an appropriately sized model for the specific task avoids unnecessary scaling. Early evaluation of outputs helps identify when a lower-cost option performs adequately. Adjusting prompt phrasing, simplifying requests, and iterating gradually can lower token use without compromising usefulness. The guidance aims to help users make consistent improvements from their initial prompts through functional agents.
