prompt-engineering

Prompt Engineering

Overview

Use this skill to design prompt systems that are clear, testable, and reusable. It covers prompt drafting, optimization, evaluation, and production-oriented patterns for few-shot prompting, reasoning workflows, templates, and system prompts.

Keep the main workflow in this file and load the targeted reference files only for the pattern you are applying.

When to Use

Use this skill when:

• A user asks to write, rewrite, or improve a prompt
• A prompt needs better structure, reliability, or output formatting
• Few-shot examples or reasoning scaffolds are needed
• A system prompt or reusable prompt template must be created
• An existing prompt needs measurable optimization and testing

Read the relevant files in references/ when you need deeper guidance on a specific pattern.

Core Patterns

1. Few-Shot Learning

Example Selection Strategy

• Use references/few-shot-patterns.md for comprehensive selection frameworks
• Balance example count (3-5 optimal) with context window limitations
• Include edge cases and boundary conditions in example sets
• Prioritize diverse examples that cover problem space variations
• Order examples from simple to complex for progressive learning

Few-Shot Example (Sentiment Classification)

Classify the sentiment as Positive, Negative, or Neutral.

Text: "I love this product! It exceeded my expectations."
Sentiment: Positive
Reasoning: Enthusiastic language, positive adjectives, satisfaction

Text: "The app keeps crashing when I upload large files."
Sentiment: Negative
Reasoning: Complaint about functionality, frustration indicator

Text: "It arrived on time, as described."
Sentiment: Neutral
Reasoning: Factual statement, no strong emotion either way

Text: "{user_input}"
Sentiment:
Reasoning:

2. Chain-of-Thought Reasoning

Implementation Patterns

• Reference references/cot-patterns.md for detailed reasoning frameworks
• Use "Let's think step by step" for zero-shot CoT initiation
• Provide complete reasoning traces for few-shot CoT demonstrations
• Implement self-consistency by sampling multiple reasoning paths
• Include verification and validation steps in reasoning chains