How to Actually Tokenmaxx and Save Millions — Alvin Pane, OutRival

This post details a practical workflow for token optimization in LLM applications, focusing on cost reduction while maintaining quality. It introduces a three-step process: 1. Establish observability by recording prompt versions, model choices, tokens, latency, and costs. 2. Optimize instructions using tools like DSPy and GEPA to find shorter prompts that preserve the interface and scoring contract. 3. Conduct rigorous shadow evaluations by comparing production to itself, candidate models to themselves, and both systems on the same requests. Human review of disagreements is crucial for identifying regressions and improvements, with paired confidence intervals and separate checks for critical customer categories serving as release criteria. Continuous measurement post-release is emphasized to ensure sustained performance.