senior-prompt-engineer

You can evaluate RAG quality by assessing retrieval-augmented generation systems to measure their accuracy and effectiveness. The Skill provides script-based analysis to validate retrieval outputs and ensure robust AI application performance.

Agent orchestration visualizes and validates agent workflows and tool configurations to ensure robust system design. This allows you to structure complex AI interactions and verify tool integration before deployment.

Generating few-shot examples and structured outputs involves analyzing prompt patterns to create consistent LLM responses. The Skill facilitates structured output generation and token usage analysis to standardize your AI application behavior.

The prompt engineering evaluation requires specific script dependencies including prompt_optimizer.py, rag_evaluator.py, and agent_orchestrator.py. These scripts enable structured analysis, visualization, and validation of your AI workflows.

High token costs and inconsistent outputs often result from unoptimized prompt structure and lack of structured generation. Analyzing prompt efficiency and implementing few-shot examples can clarify instructions and reduce unnecessary token consumption.