Technical Guide to LLM Token Optimization Press enter or click to view image in full size Why this Guide ? How a 6-week Airbnb project revealed the blueprint for cutting AI costs 60–80% — backed by research, not guesswork. In 2024, Airbnb’s engineering team faced a choice that most technical leaders quietly dread. They had 3,500 React test files sitting in legacy code. Migrating them manually would take an estimated 1.5 years and a significant chunk of engineering budget. So they did something different: they built an LLM-driven pipeline to handle it automatically. Six weeks later, 97% of the files were migrated. Done. But here’s the part that doesn’t make the headline: they didn’t just throw the most expensive frontier model at every file and let it rip. They built a cost-disciplined system — smaller models attempting each file first, validation errors fed back as dynamic prompts, escalation to expensive models only when genuinely needed. The architecture was as deliberate about cost as it was about speed.…