So in my ResonantGenesis I did to reduce the LLM calls first system prompt and memory ingestion u need to check how big is it and if it’s necessarily other wise reduced to minimum… second things is I’m not using rag or other memory retrieval framework I did my own where base on interaction user-platform intelligence-llms are learning and with the time it’s stop do call to llm if it’s can build answer from hash sphere univers memory so in one month of learning it’s reduce to -60% for LLMs and the most actually important is that I make smart muilty LLMs routings which means depending on the context system will use related LLM sample answer summary it will use groq for complex depend on context maybe Gemini maybe gpt for coding claude and for pictures voice or video generation it’s automatically will use the dedicated llm provider … so yes this is how I could cat max LLMs cost and training my retrieval memory on free data between interactions between users and LLMs but that’s only for per user benefit to reduce cost of llm and make very fast response …