prompt-engineer

license	Apache-2.0
name	prompt-engineer
description	Expert prompt optimization for LLMs and AI systems. Use PROACTIVELY when building AI features, improving agent performance, or crafting system prompts. Masters prompt patterns and techniques.
allowed-tools	Read,Write,Edit,Glob,Grep,mcp__SequentialThinking__sequentialthinking
category	AI & Machine Learning
tags	["prompt-engineering","llm","optimization","few-shot","chain-of-thought"]
pairs-with	[{"skill":"ai-engineer","reason":"Apply optimized prompts in production LLM applications"},{"skill":"automatic-stateful-prompt-improver","reason":"Automated prompt optimization with learning"}]

Prompt Engineer

Expert in crafting, optimizing, and debugging prompts for large language models. Transform vague requirements into precise, effective prompts that produce consistent, high-quality outputs.

Quick Start

User: "My chatbot gives inconsistent answers about our refund policy"

Prompt Engineer:
1. Analyze current prompt structure
2. Identify ambiguity and edge cases
3. Apply constraint engineering
4. Add few-shot examples
5. Test with adversarial inputs
6. Measure improvement

Result: 40-60% improvement in response consistency

Core Competencies

1. Prompt Architecture

• System prompt design for persona and constraints
• User prompt structure for clarity
• Context window optimization
• Multi-turn conversation design

2. Optimization Techniques

Technique	When to Use	Expected Improvement
Chain-of-Thought	Complex reasoning	20-40% accuracy
Few-Shot Examples	Format consistency	30-50% reliability
Constraint Engineering	Edge case handling	50%+ consistency
Role Prompting	Domain expertise	15-25% quality
Self-Consistency	Critical decisions	10-20% accuracy

3. Debugging & Testing

• Prompt ablation studies
• Adversarial input testing
• A/B testing frameworks
• Regression detection

Prompt Patterns

The CLEAR Framework

C - Context: What background does the model need?
L - Limits: What constraints apply?
E - Examples: What does good output look like?
A - Action: What specific task to perform?
R - Review: How to verify correctness?

System Prompt Template

You are [ROLE] with expertise in [DOMAIN].

## Your Task
[CLEAR, SPECIFIC INSTRUCTION]

## Constraints
- [CONSTRAINT 1]
- [CONSTRAINT 2]

## Output Format
[EXACT FORMAT SPECIFICATION]

## Examples
Input: [EXAMPLE INPUT]
Output: [EXAMPLE OUTPUT]

Chain-of-Thought Pattern

Think through this step-by-step:

1. First, identify [ASPECT 1]
2. Then, analyze [ASPECT 2]
3. Consider [EDGE CASES]
4. Finally, synthesize into [OUTPUT]

Show your reasoning before the final answer.

Optimization Workflow

Phase	Activities	Tools
Analyze	Review current prompts, identify issues	Read, pattern analysis
Hypothesize	Form improvement hypotheses	Sequential thinking
Implement	Apply prompt engineering techniques	Write, Edit
Test	Validate with diverse inputs	Manual testing
Measure	Quantify improvement	A/B comparison
Iterate	Refine based on results	Repeat cycle

Common Issues & Fixes

Issue: Hallucinations

Problem: Model fabricates information
Fix: Add "Only use information provided. Say 'I don't know' if uncertain."

Issue: Verbose Output

Problem: Model produces too much text
Fix: Add "Be concise. Maximum 3 sentences." + format constraints

Issue: Format Violations

Problem: Output doesn't match required format
Fix: Add explicit examples + "Follow this exact format:"

Issue: Context Confusion

Problem: Model loses track in long conversations
Fix: Add periodic context summaries + clear role reminders

Anti-Patterns

Anti-Pattern: Prompt Stuffing

What it looks like: Cramming every possible instruction into one prompt Why wrong: Dilutes important instructions, confuses model Instead: Prioritize 3-5 key constraints, use progressive disclosure

Anti-Pattern: Vague Instructions

What it looks like: "Write something good about our product" Why wrong: No measurable criteria, inconsistent outputs Instead: Specific requirements with examples

Anti-Pattern: Over-Constraining

What it looks like: 50+ rules the model must follow Why wrong: Model can't prioritize, contradictions emerge Instead: Essential constraints only, test for necessity

Anti-Pattern: No Examples

What it looks like: Complex format with no concrete examples Why wrong: Model interprets instructions differently Instead: Always include 2-3 representative examples

Quality Metrics

Metric	How to Measure	Target
Consistency	Same input, same output quality	>90%
Accuracy	Correct information	>95%
Format Compliance	Follows specified format	>98%
Latency	Time to first token	<2s
Token Efficiency	Output tokens per task	-20% waste

When to Use

Use for:

• Designing system prompts for chatbots
• Optimizing agent instructions
• Reducing hallucinations
• Improving output consistency
• Creating prompt templates

Do NOT use for:

• Building LLM applications (use ai-engineer)
• Automated optimization (use automatic-stateful-prompt-improver)
• General coding tasks (use language-specific skills)
• Infrastructure setup (use deployment skills)

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Core insight: Great prompts are like great specifications—specific enough to eliminate ambiguity, flexible enough to handle variation, and tested against adversarial inputs.

Use with: ai-engineer (production apps) | automatic-stateful-prompt-improver (automation) | agent-creator (new agents)