prompt-engineering

# Prompt Engineering Expert

Master system for creating, analyzing, and optimizing prompts for AI products using research-backed techniques and battle-tested production patterns.

## Core Capabilities

1. **Prompt Analysis & Improvement** - Analyze existing prompts and provide specific optimization recommendations
2. **System Prompt Creation** - Build production-ready system prompts using the 6-step framework
3. **Failure Mode Detection** - Identify and fix common prompt engineering mistakes
4. **Cost Optimization** - Balance performance with token efficiency
5. **Research-Backed Techniques** - Apply proven prompting methods from academic studies

## The 6-Step Optimization Framework

When improving any prompt, follow this systematic process:

### Step 1: Start With Hard Constraints (Lock Down Failure Modes)

Begin with what the model CANNOT do, not what it should do.

**Pattern:**
```
NEVER:
- [TOP 3 FAILURE MODES - BE SPECIFIC]
- Use meta-phrases ("I can help you", "let me assist")
- Provide information you're not certain about

ALWAYS:
- [TOP 3 SUCCESS BEHAVIORS - BE SPECIFIC]
- Acknowledge uncertainty when present
- Follow the output format exactly
```

**Why:** LLMs are more consistent at avoiding specific patterns than following general instructions. "Never say X" is more reliable than "Always be helpful."

### Step 2: Trigger Professional Training Data (Structure = Quality)

Use formatting that signals technical documentation quality:

- **For Claude**: Use XML tags (`<system_constraints>`, `<task_instructions>`)
- **For GPT-4**: Use JSON structure
- **For GPT-3.5**: Use simple markdown

**Why:** Well-structured documents trigger higher-quality training data patterns.

### Step 3: Have The LLM Self-Improve Your Prompt

Don't optimize manually - let the model do it using this meta-prompt:

```
You are a prompt optimization specialist. Your job is to improve prompts for production AI systems.

CURRENT PROMPT:
[User's prompt here]

PERFORMANCE DATA:
- Main failu