senior-prompt-engineer

Production-grade prompt design applies few-shot learning and pattern-based instructions to optimize LLM performance. It ensures reliable outputs by providing structured prompt strategies, evaluation methods, and governance for AI products across multiple LLMs like Claude and GPT-4.

RAG integration with agent-based workflows combines retrieval mechanisms with system-architecture guidance to orchestrate scalable AI systems. This approach grounds LLM responses in factual data, improving accuracy and reliability for complex AI product development tasks.

Few-shot prompts work effectively with Claude, GPT-4, and other LLMs for AI product development. They provide pattern-based examples that guide the model's output format and reasoning, ensuring consistent and reliable performance across different language models.

Prompt evaluation in scalable LLM orchestration involves testing instruction sets against defined patterns and expected outcomes. It systematically measures LLM performance, ensuring prompt strategies remain reliable and governed across multiple agents and AI product workflows.

Pattern-based prompts are needed for agent design when building complex, multi-step AI workflows that require reliable instruction sets. They provide structured templates that ensure consistent LLM behavior, crucial for production-grade AI systems and scalable orchestration.