prompt-engineer

```

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

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

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

```markdown

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

[CLEAR, SPECIFIC INSTRUCTION]

• [CONSTRAINT 1]
• [CONSTRAINT 2]

[EXACT FORMAT SPECIFICATION]

Input: [EXAMPLE INPUT]

Output: [EXAMPLE OUTPUT]

```

Chain-of-Thought Pattern

```markdown

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.

```

| 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 |

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-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

| 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 |

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)