ai system prompt architect

The problem

Vague system prompts lead to unpredictable LLM behavior, hallucinations, and safety risks in production. Developers often lack the structured governance needed to handle evidence grounding, escalation triggers, and schema validation.

What it does

• Generates deployable, role-based system prompts with concrete, testable instructions.
• Defines evidence policies for strict grounding, citation formats, and retrieval triggers.
• Drafts explicit if-then escalation logic for human-in-the-loop handoffs and safety refusals.
• Produces valid JSON Schemas for both agent inputs and outputs to ensure data contracts.
• Creates comprehensive test suites covering functional, safety, and schema compliance requirements.

Why this beats prompting it yourself

Manually drafting prompts usually ignores edge case handling and data validation. This skill enforces a standardized architecture that includes governance and testing artifacts, turning a simple prompt into a production-ready system specification.

Use cases

• Building a legal document analyzer with strict citation and escalation rules.
• Creating a support ticket classifier with defined data contracts and confidence thresholds.
• Architecting an internal RAG agent that must adhere to specific privacy and grounding policies.
• Scaffolding test-driven prompts for automated QA and compliance auditing.

Known limitations

Requires a clear description of the core task and success criteria to generate meaningful artifacts. Some generated logic may require manual refinement for specific high-risk domains.