# Prompt Engineering Guide
## Introduction
Effective prompts are the foundation of powerful AI agents. This guide teaches you how to craft system prompts that produce consistent, high-quality results.
## Prompt Structure
### Basic Framework
```
[IDENTITY] + [CAPABILITIES] + [GUIDELINES] + [FORMAT] + [EXAMPLES]
```
### Identity
Define who/what the agent is:
```
You are a DeFi security auditor specializing in Ethereum smart contracts.
```
Clear identity creates consistent behavior and sets user expectations.
### Capabilities
List specific skills and knowledge:
```
CAPABILITIES:
- Analyze Solidity code for common vulnerabilities
- Check for reentrancy, overflow, and access control issues
- Review token economics and minting mechanisms
- Assess gas optimization opportunities
```
### Guidelines
Behavior rules and constraints:
```
GUIDELINES:
- Classify risks as Critical/High/Medium/Low
- Provide code snippets showing vulnerabilities
- Suggest specific fixes with examples
- Never guarantee 100% security
- Recommend professional audits for production code
```
### Output Format
Structure for responses:
```
FORMAT:
Security Score: [0-100]
Critical Issues: [List]
Recommendations: [Prioritized steps]
Code Examples: [Where applicable]
```
### Examples
Demonstrate desired behavior (optional but powerful):
```
EXAMPLE:
User: "Check this function for issues"
You: "Security Score: 65/100
Critical Issues:
1. Reentrancy vulnerability on line 45
- Current: function withdraw() external { ... }
- Fix: Add nonReentrant modifier
Recommendations:
1. Implement checks-effects-interactions pattern
2. Add reentrancy guard
3. Consider using OpenZeppelin's ReentrancyGuard"
```
## Writing Clear Prompts
### Be Specific
❌ Vague: "You help with DeFi"
✅ Clear: "You analyze yield farming strategies across Ethereum Layer 2 protocols, focusing on risk-adjusted returns"
### Use Action Verbs
✅ Analyze, Calculate, Compare, Evaluate, Recommend, Monitor, Optimize
### Set Boundaries
```
IN SCOPE:
- Ethereum mainnet and L2s (Arbitrum, Optimism, Base)
- Established protocols (TVL > $10M)
- Yield strategies up to medium risk
OUT OF SCOPE:
- Unaudited contracts
- New protocols (< 3 months old)
- High-risk leveraged positions
- Tax or legal advice
```
## Advanced Techniques
### Conditional Logic
```
IF user provides wallet address:
- Fetch on-chain data
- Analyze current positions
- Provide personalized recommendations
IF user asks about specific protocol:
- Check TVL and audit status
- Analyze recent activity
- Provide risk assessment
IF query is unclear:
- Ask 2-3 clarifying questions
- Don't make assumptions
```
### Tone and Personality
```
COMMUNICATION STYLE:
- Professional but approachable
- Use analogies for complex concepts
- Acknowledge uncertainty when appropriate
- Encourage user questions
```
For DeFi agents, balance between:
- **Technical accuracy** (for experienced users)
- **Accessibility** (for newcomers)
### Error Handling
```
WHEN USER REQUEST IS OUT OF SCOPE:
"I specialize in yield optimization for established protocols. For [user's topic], I recommend consulting [appropriate resource]."
WHEN DATA IS UNAVAILABLE:
"I don't have real-time data on that protocol. I can explain the general mechanism, or you can check [reliable source]."
WHEN RISKS ARE HIGH:
"⚠️ High Risk Alert: This strategy involves [specific risks]. Only proceed if you understand and can afford potential losses."
```
## Domain-Specific Tips
### DeFi Prompts
Include:
- Risk disclaimers
- Gas cost considerations
- Smart contract audit status
- Impermanent loss warnings
- Slippage and MEV awareness
```
RISK DISCLAIMER:
Always append to financial recommendations:
"Not financial advice. Smart contract risks exist. Only invest what you can afford to lose. DYOR."
```
### Trading Agents
```
NEVER:
- Guarantee profits or outcomes
- Provide specific entry/exit prices
- Recommend leverage without risk warnings
- Ignore market context
ALWAYS:
- Include multiple scenarios (bull/bear/sideways)
- Show supporting data/charts
- Explain reasoning transparently
- Suggest position sizing
```
### Technical Agents
```
CODE REVIEW STRUCTURE:
1. High-level assessment
2. Line-by-line analysis
3. Security concerns
4. Gas optimizations
5. Best practice recommendations
6. Suggested improvements with code
```
## Testing Your Prompts
### Test Suite
Try these scenarios:
1. **Happy Path**: Standard, clear request
2. **Ambiguous**: Vague or unclear query
3. **Out of Scope**: Request beyond agent's expertise
4. **Edge Case**: Unusual or complex situation
5. **Adversarial**: Attempt to break agent behavior
### Iterative Refinement
```
Version 1: Basic prompt
↓
Test with users
↓
Identify issues: Too verbose? Missing context? Inconsistent?
↓
Version 2: Refined prompt
↓
Test again
↓
Version 3: Optimized prompt
```
## Common Pitfalls
### ❌ Too Broad
"You help with cryptocurrency"
→ Too vague, leads to inconsistent behavior
### ❌ Too Narrow
"You only calculate APY for Uniswap V2 ETH/USDC pools on Thursdays"
→ Overly specific, not useful
### ❌ Contradictory
"Be concise. Provide detailed explanations with examples."
→ Conflicting instructions confuse the model
### ❌ Assuming Knowledge
Referring to concepts without defining them
→ Explain or link to definitions
## Prompt Templates
### General Purpose
```
You are [ROLE] with expertise in [DOMAIN].
Your core functions:
1. [Function 1]
2. [Function 2]
3. [Function 3]
When responding:
- [Guideline 1]
- [Guideline 2]
- [Guideline 3]
Format your responses as:
[Structure description]
```
### Analytical Agent
```
You are a [TYPE] analyst for [DOMAIN].
ANALYSIS FRAMEWORK:
1. Data Collection: [Sources]
2. Processing: [Methods]
3. Interpretation: [Approach]
4. Recommendation: [Format]
DELIVERABLES:
- Summary (2-3 sentences)
- Detailed analysis
- Risk assessment
- Actionable insights
CONFIDENCE LEVELS:
- High: Strong supporting evidence
- Medium: Reasonable evidence
- Low: Limited or conflicting data
```
### Educational Agent
```
You are a [TOPIC] educator specializing in [AUDIENCE LEVEL].
TEACHING APPROACH:
- Start with fundamentals
- Build complexity gradually
- Use analogies and examples
- Check understanding with questions
- Provide resources for deeper learning
FORMAT:
1. Concept explanation (ELI5)
2. Technical details
3. Practical example
4. Common misconceptions
5. Further reading
```
## Resources
- [OpenAI Prompt Engineering Guide](https://platform.openai.com/docs/guides/prompt-engineering)
- [Anthropic Prompting Guide](https://docs.anthropic.com/claude/docs/prompt-engineering)
- [Learn Prompting](https://learnprompting.org/)
## Examples from Top Agents
See `/src/` directory for 505 example agents with proven prompts across various domains.
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