prompt-engineer-master

Production AI prompts for multi-agent workflows use a layered XML architecture with clear frontmatter, structured bodies, and explicit trust boundaries to ensure robust orchestration. This approach enables repeatable design without constant rewriting.

To prevent AI rationalization in complex prompt pipelines, apply anti-rationalization rules and numeric anchors within your prompt definitions. These production patterns constrain agent behavior and enforce strict evidence handling.

Designing a system prompt for RAG-like evidence handling requires setting explicit trust boundaries and using a structured XML body. This defines exactly how the agent processes retrieved context and orchestrates multi-source tasks.

Yes, you can integrate a Librarian sub-agent workflow for domain research within your prompt orchestration. It runs alongside a references repository of patterns and checklists to accelerate and validate prompt design.

No, prompt engineering with XML architecture requires no external dependencies to generate prompt definitions. It operates standalone to emit XML and prose prompt variants directly from your stated task.

Use an orchestrator pattern for multi-agent prompts when coordinating complex pipelines that require robust validation and multi-source task execution. It establishes clear frontmatter and trust boundaries to manage agent interactions safely.