prompt-engineering

AI prompt engineering structures system prompts by defining explicit roles, contexts, tasks, and constraints. This reduces ambiguity and ensures reliable outputs from AI agents in MCP-driven workflows.

Chain-of-thought prompts guide AI models to articulate intermediate reasoning steps before producing a final answer. Use this pattern for complex tasks requiring logical deduction to improve output reliability and safety.

Create MCP tool descriptions by applying structured prompt templates that standardize usage and define explicit inputs, outputs, and constraints. This ensures AI agents correctly understand and invoke available tools.

The best way to test AI prompts is to apply evaluation rubrics and testing guidelines specifically designed for prompt patterns. This verifies safety considerations and measures output consistency against defined criteria.

Prompt engineering requires a SKILL.md file containing YAML frontmatter with name and description fields. This structured format provides the comprehensive prompt patterns and testing guidance needed for MCP-driven agent ecosystems.

AI prompts produce inconsistent outputs due to missing explicit constraints, unclear roles, or ambiguous task definitions. Rewriting them using structured patterns like Few-Shot or Role+Task standardizes inputs and evaluation criteria.