senior-prompt-engineer

Designing prompts for agent architectures involves structuring reusable templates for multi-step tasks. You apply patterns like ReAct or Plan-and-Execute to orchestrate tools, enabling memory-enabled workflows that execute complex multi-tool tasks effectively.

LLM evaluation in prompt engineering requires defining specific metrics to assess clarity, fidelity, safety, and consistency. You establish testing strategies to measure these dimensions against a baseline, ensuring your prompt templates produce reliable and safe structured outputs.

Integrating RAG with structured outputs requires combining retrieval-augmented generation with parseable result formats. You apply validation checks to the retrieved data and generated responses, ensuring the LLM returns accurate, structured information that satisfies your application requirements.

Use few-shot prompt design when zero-shot templates fail to produce consistent structured outputs or follow complex instructions. Few-shot design provides explicit examples within the prompt template, guiding the LLM to replicate patterns for tasks requiring high fidelity and specific formatting.

Analyzing token usage for multi-tool workflows involves evaluating prompt templates and agent designs to measure token consumption. You assess inputs and outputs across orchestration steps, optimizing the prompt structure to reduce costs while maintaining safety and cross-tool performance.