Prompt Optimization for Cost: Cutting Tokens Without Cutting Quality
Prompt cost compounds with traffic, so token optimization pays off fast. Here is how I cut tokens without cutting quality. Prompts cost money, and in production the cost scales with traffic. A prompt that is twice as long as it needs to be costs twice as much per call, and across thousands of calls that adds up fast. Prompt optimization for cost is the practice of reducing token usage without reducing output quality, and it is one of the highest-use optimizations in an LLM application because it compounds with every call. After cutting the cost of several production prompts by large margins, I have a set of techniques that reduce tokens without hurting results. This guide covers them. Measure Before Optimizing I never optimize prompt cost blindly. I log the token count of every prompt and its output, and I look at the distribution before changing anything. Most cost is in a small number of prompts, and optimizing the cheap ones wastes effort. The log tells me which prompts to target, what their token counts actually are, and how much room there is. Optimization without measurement is guessing, and guesses about token cost are usually wrong. Log input and output tokens for every call. Find the expensive prompts that account for most cost. Measure the baseline before any change. Re-measure after changes to confirm savings are real.