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 tools.
Works with: Claude Code, Cursor, Codex CLI
Category: Productivity — see all ranked ›
Work: Prompt engineering · Model evaluation · Retrieval systems
Who it is for: AI engineer
- Adoption: 1 repos
- Health: active
- GitHub stars: 23,181
- Contributors: 30
- License: MIT
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source ↗ · skill:alirezarezvani/senior-prompt-engineer
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