prompt-engineer_skill

Overview

This skill is a practical guide to prompt engineering best practices for designing high-quality prompts, system messages, and prompt strategies for AI applications. It helps users structure prompts, control output formats, and reduce hallucinations. The guidance is hands-on and suited for developers, product managers, and AI practitioners seeking consistent LLM outputs.

How this skill works

The skill inspects prompts and system messages, evaluates clarity, context, and output constraints, and suggests concrete rewrites or templates. It provides role-based and few-shot templates, chain-of-thought patterns, and JSON/structured-output controls. It also includes diagnostic techniques to iterate and measure prompt performance.

When to use it

• Designing system prompts for an AI assistant or product
• Improving output consistency and reducing hallucinations
• Creating few-shot examples and role definitions for specialized tasks
• Specifying output formats (JSON, CSV, bullet lists) for downstream parsing
• Optimizing prompts for classification, summarization, or code generation

Best practices

• Be explicit: state role, task, context, constraints, and exact output format
• Provide minimal but sufficient context; include examples for ambiguous tasks
• Use role prompting to set persona and capabilities when domain expertise is needed
• Iterate: test variations, measure outputs, and refine prompts based on failure modes
• Constrain hallucinations: require citations, limit scope to provided info, and ask model to admit uncertainty
• Control structure: request machine-parseable formats (JSON schema) and include examples

Example use cases

• Customer support: craft system prompt + few-shot examples to classify and triage tickets
• Code review: role-based prompt that requests annotated diffs, potential bugs, and improvements
• Summarization: structured prompts that return concise summary, key points, and confidence score in JSON
• Data extraction: specify field schema and examples so the model returns consistent, parseable records
• Product design: use chain-of-thought template to generate options, pros/cons, and recommended next steps