Five pipeline optimizations that slashed LLM token usage by 70% without quality loss

A development team found that rising API costs, inconsistent latency, and over-powered model usage were draining their LLM budget within weeks of deployment. Rather than simply downgrading to a cheaper model, they optimized the entire pipeline using techniques such as complexity-based routing, which directs simple tasks to lighter models and reserves expensive ones for advanced reasoning. They also trimmed bloated prompts, implemented semantic caching to serve repeated intents without re-calling the LLM, and filtered RAG context more aggressively to send only the most relevant document chunks. Adopting structured outputs further reduced unnecessary token generation and improved parsing reliability. Together, these changes cut token usage and API spend by roughly 70% while keeping response quality largely intact.