Rollout checklist
Define the target waste source
Choose one starting point: RAG context, retries, model routing, semantic cache, GPU serving or training cost.
Collect baseline telemetry
Capture current spend, token counts, request volume, retry behavior, latency, model mix and key workflows.
Set integrity guardrails
Define protected facts, citation requirements, policy limits and failure conditions before optimizing.
Run a limited pilot
Test on a bounded set of workflows and compare before/after cost, latency and answer quality.
Measure safe savings
Count savings only when the answer remains reliable, current and policy-safe.
Expand by maturity level
Move from Observe to Optimize, Control, ModelOps or Lifecycle based on evidence.
Next step
Turn the checklist into a pilot plan
Request a free audit and ML Mind will map the first safe control layer for your stack.