senior-prompt-engineer

To design prompts for multi-step reasoning, apply ReAct and Plan-and-Execute patterns to structure tool use and sequential reasoning. These agent design patterns help manage memory-aware prompts and orchestrate complex workflows for robust LLM task execution.

The best way to enforce structured output generation is to specify a strict output format within the prompt skeleton. This approach ensures reliable extraction and few-shot example design, maintaining predictable LLM behavior across diverse tasks.

To evaluate LLM prompts, apply evaluation frameworks that provide quality scoring and A/B testing readiness. This process involves testing prompt reliability and efficiency to refine behavior and ensure safe performance across diverse tasks.

Yes, you can orchestrate multi-agent workflows using agent design architectures that integrate tools and guardrails. This method supports both single and multi-agent configurations, ensuring robust execution and safe behavior in complex LLM workflows.

You should avoid few-shot extraction when strict structured output enforcement is the primary requirement over contextual learning. In such cases, defining a rigid prompt skeleton with explicit role and tool specifications yields more reliable LLM behavior.