curiositech/prompt-engineer

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)