"Your RAG Pipeline Wastes 64% of Tokens on Documents You Already Sent — Here's the Fix"

"Your RAG Pipeline Wastes 64% of Tokens on Documents You Already Sent — Here's the Fix" Tags : #LLM #OpenAI #RAG #TokenOptimization #VSCode #DevTools We tested 9,300 real documents across 4 categories: RAG chunks, pull requests, emails, and support tickets. The results were painful: RAG documents : 64% redundancy (your retriever keeps fetching the same chunks) Pull requests : 64% redundancy (similar diffs, repeated file contexts) Emails : 62% redundancy (reply chains, signatures, boilerplate) Support tickets : 26% redundancy (templates, repeated issue descriptions) On average, 44% of tokens you send to LLM APIs are content you've already sent before . You're paying for the same information twice. Sometimes three times. Sometimes ten. Why existing solutions don't fix this Prompt caching (OpenAI, Anthropic) sounds like the answer. But in production agentic workflows — LangChain chains, CrewAI agents, AutoGen pipelines — the cache hit rate drops below 20%. Why? Because every request carri