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)

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)