How I Used Rust and Reinforcement Learning to Slash LLM Token Usage by 40%

Building AI agents that need to process massive amounts of code or text usually leads to one major bottleneck: Context Window Bloat. When building complex RAG (Retrieval-Augmented Generation) applications, developers often resort to stuffing as much information into the context window as possible. This naive approach leads to massive token usage, slower response times, and LLMs getting "lost in the middle" and degrading in reasoning accuracy. I built Entroly, an open-source (MIT licensed) Cont