Senior Prompt Engineer

Senior Prompt Engineer

Use when the user asks to optimize prompts, design prompt templates, evaluate LLM outputs with an eval set, measure RAG retrieval quality, validate agent/tool configurations, analyze token usage, or design structured-output contracts. Covers eval-driven prompt iteration, RAG metrics (relevance, faithfulness, coverage), agent workflow validation, and token/cost budgeting — all model-agnostic, with three stdlib Python

Optimized workflow

This edition turns the source methodology into a repeatable agent workflow with explicit inputs, checkpoints and deliverables.

Quality standard

• Confirm scope and missing inputs before execution
• Ground decisions in available evidence and preserve source constraints
• Return an actionable result with assumptions, risks and next steps

Agent compatibility

The same core method is packaged for Claude, Codex, GPT, Gemini, Cursor and OpenCode.