A surprising insight from our experience is that optimizing token usage often hinges more on your prompt engineering than on tweaking the LLM itself. By refining prompts to be more concise and specific, teams have seen cost reductions of up to 30%. One practical framework is to iteratively test and refine prompts with a focus on clarity and brevity. This approach not only lowers costs but also improves model performance by reducing unnecessary token processing. - Ali Muwwakkil (ali-muwwakkil on LinkedIn)