RAG cost is a context selection problem
RAG systems often retrieve more chunks than the model truly needs. Sending all chunks increases token cost, latency and the chance that old or irrelevant sources influence the answer.
ML Mind evaluates relevance, trust, freshness, citation value and protected facts before deciding which chunks should reach the model.
Try the simulator
The RAG simulator shows how fewer, better chunks can reduce cost while preserving citations and important facts. Use it to explain the difference between compression and safe context optimization.
How to apply this with ML Mind
Use this topic as a discovery lens. Start by identifying the workflow, measuring the current waste pattern, then deciding whether the right control is visibility, pre-model optimization, full gateway control, ModelOps serving control or lifecycle governance.