Bringing The Receipts - 95% AI LLM Token Savings

95% Token Reduction, 96% Precision: Benchmarking jCodeMunch Against Chunk RAG and Naive File Reading Here are the benchmarks... AND the bench! TL;DR: Across 15 tasks on 3 real repos, structured MCP symbol retrieval achieves 95% avg token reduction vs naive file reading — while hitting 96% precision vs 74% for chunk RAG. The benchmark harness is open-source. You can reproduce every number in under 5 minutes. This is a follow-up to Your AI Agent Is Dumpster Diving Through Your Code — the argument there is the setup for the proof here. Worth 5 minutes if you haven't read it. Must-read setup: The first article lays out why file-reading agents waste tokens structurally — not because they're badly written, but because reading whole files is the wrong unit of retrieval for code. This article is the empirical test of that claim. Last time, I argued that AI coding agents waste absurd amounts of tokens rummaging through whole files and sloppy chunks. Fair enough. Big claim. So here are the recei