ai-token-improvement-plan-engineer

Guides creation of an AI token and cost-improvement plan—baseline audits, spend attribution, optimization-initiative backlog (prompt, context, model routing, RAG, agents), impact estimates, quality guardrails, measurement KPIs, and phased rollout with owners. Use when building a token-reduction roadmap, cost-optimization program, LLM unit-economics improvement plan, or executive brief on cutting inference spend without breaking evals—not for hands-on context layout implementation (ai-context-engineer), single-prompt rewrites (prompt-engineer), RAG pipeline build (ai-engineer), or AI ops cadence and vendor governance (ai-lead-ops). For end-to-end commercial/enterprise AI solution architecture before cost programs, use applied-ai-architect-commercial-enterprise. Empirical token studies and ablations: research-engineer-scientist-tokens.