Senior Prompt Engineer

Prompt engineering patterns, LLM evaluation frameworks, and agentic system design. Provides static (deterministic) analysis tools to optimize prompts, evaluate RAG retrieval and generation quality, and validate/visualize agent workflows — plus deep reference libraries of prompt patterns, evaluation metrics, and agent architectures.

Core Capabilities

• Prompt optimization — token counting and cost estimation, clarity/structure scoring, ambiguity and redundancy detection, and generation of optimized prompt versions.
• Few-shot & structured output design — extract/manage few-shot examples, design diverse example sets (simple/edge/complex/negative), and enforce reliable JSON/XML schema outputs.
• RAG evaluation — context relevance, answer faithfulness, groundedness (ROUGE-L), and retrieval metrics (Precision@K, MRR, NDCG) over pre-retrieved contexts.
• Agentic system design — validate agent configs, visualize flows (ASCII/Mermaid), estimate token cost per run, and apply ReAct / Plan-Execute / Tool-Use / multi-agent patterns.
• Pattern library — 10 prompt patterns, evaluation frameworks (A/B testing, benchmarks, human eval), and agent architectures with pseudocode.

When to Use

• Optimizing an existing prompt's performance or reducing token costs.
• Designing prompt templates, few-shot examples, or structured-output workflows.
• Evaluating LLM outputs or RAG retrieval/generation quality.
• Building or validating agentic systems and tool-calling workflows.

Tools