ML Mind · AI FinOps
RAG Cost Optimization
Many RAG systems retrieve useful evidence, then send too much of it to the model. ML Mind helps select the right evidence before inference.
Review your RAG costWhy this matters
The problem with naive RAG
A pipeline may retrieve 15 chunks when only 5 are necessary. Sending everything increases input cost, latency and hallucination risk.
Safe context optimization
ML Mind protects numbers, dates, policies, source references and critical instructions while reducing redundant or low-trust context.
Freshness-aware retrieval control
Chunk selection should consider not only semantic similarity, but also source freshness, trust, citation need and currentness.
Where ML Mind creates savings
Token reductionRAG chunk selectionRetry preventionModel routingVerified cachingSmart fallbackGPU serving optimizationTraining cost control
Related AI cost topics
Turn this insight into a savings audit
Use your simulator result as the starting point for a free ML Mind AI FinOps audit.