"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 carries dif