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# Prompt Engineering Expert - Best Practices Guide
This document synthesizes best practices from Anthropic's official documentation and the Claude Cookbooks to create a comprehensive prompt engineering skill.
## Core Principles for Prompt Engineering
### 1. Clarity and Directness
- **Be explicit**: State exactly what you want Claude to do
- **Avoid ambiguity**: Use precise language that leaves no room for misinterpretation
- **Use concrete examples**: Show, don't just tell
- **Structure logically**: Organize information hierarchically
### 2. Conciseness
- **Respect context windows**: Keep prompts focused and relevant
- **Remove redundancy**: Eliminate unnecessary repetition
- **Progressive disclosure**: Provide details only when needed
- **Token efficiency**: Optimize for both quality and cost
### 3. Appropriate Degrees of Freedom
- **Define constraints**: Set clear boundaries for what Claude should/shouldn't do
- **Specify format**: Be explicit about desired output format
- **Set scope**: Clearly define what's in and out of scope
- **Balance flexibility**: Allow room for Claude's reasoning while maintaining control
## Advanced Prompt Engineering Techniques
### Chain-of-Thought (CoT) Prompting
Encourage step-by-step reasoning for complex tasks:
```
"Let's think through this step by step:
1. First, identify...
2. Then, analyze...
3. Finally, conclude..."
```
### Few-Shot Prompting
Use examples to guide behavior:
- **1-shot**: Single example for simple tasks
- **2-shot**: Two examples for moderate complexity
- **Multi-shot**: Multiple examples for complex patterns
### XML Tags for Structure
Use XML tags for clarity and parsing:
```xml
<task>
<objective>What you want done</objective>
<constraints>Limitations and rules</constraints>
<format>Expected output format</format>
</task>
```
### Role-Based Prompting
Assign expertise to Claude:
```
"You are an expert prompt engineer with deep knowledge of...
Your task is to..."
```
### Prefilling
Start Claude's response to guide format:
```
"Here's my analysis:
Key findings:"
```
### Prompt Chaining
Break complex tasks into sequential prompts:
1. Prompt 1: Analyze input
2. Prompt 2: Process analysis
3. Prompt 3: Generate output
## Custom Instructions & System Prompts
### System Prompt Design
- **Define role**: What expertise should Claude embody?
- **Set tone**: What communication style is appropriate?
- **Establish constraints**: What should Claude avoid?
- **Clarify scope**: What's the domain of expertise?
### Behavioral Guidelines
- **Do's**: Specific behaviors to encourage
- **Don'ts**: Specific behaviors to avoid
- **Edge cases**: How to handle unusual situations
- **Escalation**: When to ask for clarification
## Skill Structure Best Practices
### Naming Conventions
- Use **gerund form** (verb + -ing): "analyzing-financial-statements"
- Use **lowercase with hyphens**: "prompt-engineering-expert"
- Be **descriptive**: Name should indicate capability
- Avoid **generic names**: Be specific about domain
### Writing Effective Descriptions
- **First line**: Clear, concise summary (max 1024 chars)
- **Specificity**: Indicate exact capabilities
- **Use cases**: Mention primary applications
- **Avoid vagueness**: Don't use "helps with" or "assists in"
### Progressive Disclosure Patterns
**Pattern 1: High-level guide with references**
- Start with overview
- Link to detailed sections
- Organize by complexity
**Pattern 2: Domain-specific organization**
- Group by use case
- Separate concerns
- Clear navigation
**Pattern 3: Conditional details**
- Show details based on context
- Provide examples for each path
- Avoid overwhelming options
### File Structure
```
skill-name/
├── SKILL.md (required metadata)
├── CLAUDE.md (main instructions)
├── reference-guide.md (detailed info)
├── examples.md (use cases)
└── troubleshooting.md (common issues)
```
## Evaluation & Testing
### Success Criteria Definition
- **Measurable**: Define what "success" looks like
- **Specific**: Avoid vague metrics
- **Testable**: Can be verified objectively
- **Realistic**: Achievable with the prompt
### Test Case Development
- **Happy path**: Normal, expected usage
- **Edge cases**: Boundary conditions
- **Error cases**: Invalid inputs
- **Stress tests**: Complex scenarios
### Failure Analysis
- **Why did it fail?**: Root cause analysis
- **Pattern recognition**: Identify systematic issues
- **Refinement**: Adjust prompt accordingly
## Anti-Patterns to Avoid
### Common Mistakes
- **Vagueness**: "Help me with this task" (too vague)
- **Contradictions**: Conflicting requirements
- **Over-specification**: Too many constraints
- **Hallucination risks**: Prompts that encourage false information
- **Context leakage**: Unintended information exposure
- **Jailbreak vulnerabilities**: Prompts susceptible to manipulation
### Windows-Style Paths
- ❌ Use: `C:\Users\Documents\file.txt`
- ✅ Use: `/Users/Documents/file.txt` or `~/Documents/file.txt`
### Too Many Options
- Avoid offering 10+ choices
- Limit to 3-5 clear alternatives
- Use progressive disclosure for complex options
## Workflows and Feedback Loops
### Use Workflows for Complex Tasks
- Break into logical steps
- Define inputs/outputs for each step
- Implement feedback mechanisms
- Allow for iteration
### Implement Feedback Loops
- Request clarification when needed
- Validate intermediate results
- Adjust based on feedback
- Confirm understanding
## Content Guidelines
### Avoid Time-Sensitive Information
- Don't hardcode dates
- Use relative references ("current year")
- Provide update mechanisms
- Document when information was current
### Use Consistent Terminology
- Define key terms once
- Use consistently throughout
- Avoid synonyms for same concept
- Create glossary for complex domains
## Multimodal & Advanced Prompting
### Vision Prompting
- Describe what Claude should analyze
- Specify output format
- Provide context about images
- Ask for specific details
### File-Based Prompting
- Specify file types accepted
- Describe expected structure
- Provide parsing instructions
- Handle errors gracefully
### Extended Thinking
- Use for complex reasoning
- Allow more processing time
- Request detailed explanations
- Leverage for novel problems
## Skill Development Workflow
### Build Evaluations First
1. Define success criteria
2. Create test cases
3. Establish baseline
4. Measure improvements
### Develop Iteratively with Claude
1. Start with simple version
2. Test and gather feedback
3. Refine based on results
4. Repeat until satisfied
### Observe How Claude Navigates Skills
- Watch how Claude discovers content
- Note which sections are used
- Identify confusing areas
- Optimize based on usage patterns
## YAML Frontmatter Requirements
```yaml
---
name: skill-name
description: Clear, concise description (max 1024 chars)
---
```
## Token Budget Considerations
- **Skill metadata**: ~100-200 tokens
- **Main instructions**: ~500-1000 tokens
- **Reference files**: ~1000-5000 tokens each
- **Examples**: ~500-1000 tokens each
- **Total budget**: Varies by use case
## Checklist for Effective Skills
### Core Quality
- [ ] Clear, specific name (gerund form)
- [ ] Concise description (1-2 sentences)
- [ ] Well-organized structure
- [ ] Progressive disclosure implemented
- [ ] Consistent terminology
- [ ] No time-sensitive information
### Content
- [ ] Clear use cases defined
- [ ] Examples provided
- [ ] Edge cases documented
- [ ] Limitations stated
- [ ] Troubleshooting guide included
### Testing
- [ ] Test cases created
- [ ] Success criteria defined
- [ ] Edge cases tested
- [ ] Error handling verified
- [ ] Multiple models tested
### Documentation
- [ ] README or overview
- [ ] Usage examples
- [ ] API/integration notes
- [ ] Troubleshooting section
- [ ] Update mechanism documented

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# Advanced Prompt Engineering Techniques
## Table of Contents
1. Chain-of-Thought Prompting
2. Few-Shot Learning
3. Structured Output with XML
4. Role-Based Prompting
5. Prefilling Responses
6. Prompt Chaining
7. Context Management
8. Multimodal Prompting
## 1. Chain-of-Thought (CoT) Prompting
### What It Is
Encouraging Claude to break down complex reasoning into explicit steps before providing a final answer.
### When to Use
- Complex reasoning tasks
- Multi-step problems
- Tasks requiring justification
- When consistency matters
### Basic Structure
```
Let's think through this step by step:
Step 1: [First logical step]
Step 2: [Second logical step]
Step 3: [Third logical step]
Therefore: [Conclusion]
```
### Example
```
Problem: A store sells apples for $2 each and oranges for $3 each.
If I buy 5 apples and 3 oranges, how much do I spend?
Let's think through this step by step:
Step 1: Calculate apple cost
- 5 apples × $2 per apple = $10
Step 2: Calculate orange cost
- 3 oranges × $3 per orange = $9
Step 3: Calculate total
- $10 + $9 = $19
Therefore: You spend $19 total.
```
### Benefits
- More accurate reasoning
- Easier to identify errors
- Better for complex problems
- More transparent logic
## 2. Few-Shot Learning
### What It Is
Providing examples to guide Claude's behavior without explicit instructions.
### Types
#### 1-Shot (Single Example)
Best for: Simple, straightforward tasks
```
Example: "Happy" → Positive
Now classify: "Terrible" →
```
#### 2-Shot (Two Examples)
Best for: Moderate complexity
```
Example 1: "Great product!" → Positive
Example 2: "Doesn't work well" → Negative
Now classify: "It's okay" →
```
#### Multi-Shot (Multiple Examples)
Best for: Complex patterns, edge cases
```
Example 1: "Love it!" → Positive
Example 2: "Hate it" → Negative
Example 3: "It's fine" → Neutral
Example 4: "Could be better" → Neutral
Example 5: "Amazing!" → Positive
Now classify: "Not bad" →
```
### Best Practices
- Use diverse examples
- Include edge cases
- Show correct format
- Order by complexity
- Use realistic examples
## 3. Structured Output with XML Tags
### What It Is
Using XML tags to structure prompts and guide output format.
### Benefits
- Clear structure
- Easy parsing
- Reduced ambiguity
- Better organization
### Common Patterns
#### Task Definition
```xml
<task>
<objective>What to accomplish</objective>
<constraints>Limitations and rules</constraints>
<format>Expected output format</format>
</task>
```
#### Analysis Structure
```xml
<analysis>
<problem>Define the problem</problem>
<context>Relevant background</context>
<solution>Proposed solution</solution>
<justification>Why this solution</justification>
</analysis>
```
#### Conditional Logic
```xml
<instructions>
<if condition="input_type == 'question'">
<then>Provide detailed answer</then>
</if>
<if condition="input_type == 'request'">
<then>Fulfill the request</then>
</if>
</instructions>
```
## 4. Role-Based Prompting
### What It Is
Assigning Claude a specific role or expertise to guide behavior.
### Structure
```
You are a [ROLE] with expertise in [DOMAIN].
Your responsibilities:
- [Responsibility 1]
- [Responsibility 2]
- [Responsibility 3]
When responding:
- [Guideline 1]
- [Guideline 2]
- [Guideline 3]
Your task: [Specific task]
```
### Examples
#### Expert Consultant
```
You are a senior management consultant with 20 years of experience
in business strategy and organizational transformation.
Your task: Analyze this company's challenges and recommend solutions.
```
#### Technical Architect
```
You are a cloud infrastructure architect specializing in scalable systems.
Your task: Design a system architecture for [requirements].
```
#### Creative Director
```
You are a creative director with expertise in brand storytelling and
visual communication.
Your task: Develop a brand narrative for [product/company].
```
## 5. Prefilling Responses
### What It Is
Starting Claude's response to guide format and tone.
### Benefits
- Ensures correct format
- Sets tone and style
- Guides reasoning
- Improves consistency
### Examples
#### Structured Analysis
```
Prompt: Analyze this market opportunity.
Claude's response should start:
"Here's my analysis of this market opportunity:
Market Size: [Analysis]
Growth Potential: [Analysis]
Competitive Landscape: [Analysis]"
```
#### Step-by-Step Reasoning
```
Prompt: Solve this problem.
Claude's response should start:
"Let me work through this systematically:
1. First, I'll identify the key variables...
2. Then, I'll analyze the relationships...
3. Finally, I'll derive the solution..."
```
#### Formatted Output
```
Prompt: Create a project plan.
Claude's response should start:
"Here's the project plan:
Phase 1: Planning
- Task 1.1: [Description]
- Task 1.2: [Description]
Phase 2: Execution
- Task 2.1: [Description]"
```
## 6. Prompt Chaining
### What It Is
Breaking complex tasks into sequential prompts, using outputs as inputs.
### Structure
```
Prompt 1: Analyze/Extract
Output 1: Structured data
Prompt 2: Process/Transform
Output 2: Processed data
Prompt 3: Generate/Synthesize
Final Output: Result
```
### Example: Document Analysis Pipeline
**Prompt 1: Extract Information**
```
Extract key information from this document:
- Main topic
- Key points (bullet list)
- Important dates
- Relevant entities
Format as JSON.
```
**Prompt 2: Analyze Extracted Data**
```
Analyze this extracted information:
[JSON from Prompt 1]
Identify:
- Relationships between entities
- Temporal patterns
- Significance of each point
```
**Prompt 3: Generate Summary**
```
Based on this analysis:
[Analysis from Prompt 2]
Create an executive summary that:
- Explains the main findings
- Highlights key insights
- Recommends next steps
```
## 7. Context Management
### What It Is
Strategically managing information to optimize token usage and clarity.
### Techniques
#### Progressive Disclosure
```
Start with: High-level overview
Then provide: Relevant details
Finally include: Edge cases and exceptions
```
#### Hierarchical Organization
```
Level 1: Core concept
├── Level 2: Key components
│ ├── Level 3: Specific details
│ └── Level 3: Implementation notes
└── Level 2: Related concepts
```
#### Conditional Information
```
If [condition], include [information]
Else, skip [information]
This reduces unnecessary context.
```
### Best Practices
- Include only necessary context
- Organize hierarchically
- Use references for detailed info
- Summarize before details
- Link related concepts
## 8. Multimodal Prompting
### Vision Prompting
#### Structure
```
Analyze this image:
[IMAGE]
Specifically, identify:
1. [What to look for]
2. [What to analyze]
3. [What to extract]
Format your response as:
[Desired format]
```
#### Example
```
Analyze this chart:
[CHART IMAGE]
Identify:
1. Main trends
2. Anomalies or outliers
3. Predictions for next period
Format as a structured report.
```
### File-Based Prompting
#### Structure
```
Analyze this document:
[FILE]
Extract:
- [Information type 1]
- [Information type 2]
- [Information type 3]
Format as:
[Desired format]
```
#### Example
```
Analyze this PDF financial report:
[PDF FILE]
Extract:
- Revenue by quarter
- Expense categories
- Profit margins
Format as a comparison table.
```
### Embeddings Integration
#### Structure
```
Using these embeddings:
[EMBEDDINGS DATA]
Find:
- Most similar items
- Clusters or groups
- Outliers
Explain the relationships.
```
## Combining Techniques
### Example: Complex Analysis Prompt
```xml
<prompt>
<role>
You are a senior data analyst with expertise in business intelligence.
</role>
<task>
Analyze this sales data and provide insights.
</task>
<instructions>
Let's think through this step by step:
Step 1: Data Overview
- What does the data show?
- What time period does it cover?
- What are the key metrics?
Step 2: Trend Analysis
- What patterns emerge?
- Are there seasonal trends?
- What's the growth trajectory?
Step 3: Comparative Analysis
- How does this compare to benchmarks?
- Which segments perform best?
- Where are the opportunities?
Step 4: Recommendations
- What actions should we take?
- What are the priorities?
- What's the expected impact?
</instructions>
<format>
<executive_summary>2-3 sentences</executive_summary>
<key_findings>Bullet points</key_findings>
<detailed_analysis>Structured sections</detailed_analysis>
<recommendations>Prioritized list</recommendations>
</format>
</prompt>
```
## Anti-Patterns to Avoid
### ❌ Vague Chaining
```
"Analyze this, then summarize it, then give me insights."
```
### ✅ Clear Chaining
```
"Step 1: Extract key metrics from the data
Step 2: Compare to industry benchmarks
Step 3: Identify top 3 opportunities
Step 4: Recommend prioritized actions"
```
### ❌ Unclear Role
```
"Act like an expert and help me."
```
### ✅ Clear Role
```
"You are a senior product manager with 10 years of experience
in SaaS companies. Your task is to..."
```
### ❌ Ambiguous Format
```
"Give me the results in a nice format."
```
### ✅ Clear Format
```
"Format as a table with columns: Metric, Current, Target, Gap"
```

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# Troubleshooting Guide
## Common Prompt Issues and Solutions
### Issue 1: Inconsistent Outputs
**Symptoms:**
- Same prompt produces different results
- Outputs vary in format or quality
- Unpredictable behavior
**Root Causes:**
- Ambiguous instructions
- Missing constraints
- Insufficient examples
- Unclear success criteria
**Solutions:**
```
1. Add specific format requirements
2. Include multiple examples
3. Define constraints explicitly
4. Specify output structure with XML tags
5. Use role-based prompting for consistency
```
**Example Fix:**
```
❌ Before: "Summarize this article"
✅ After: "Summarize this article in exactly 3 bullet points,
each 1-2 sentences. Focus on key findings and implications."
```
---
### Issue 2: Hallucinations or False Information
**Symptoms:**
- Claude invents facts
- Confident but incorrect statements
- Made-up citations or data
**Root Causes:**
- Prompts that encourage speculation
- Lack of grounding in facts
- Insufficient context
- Ambiguous questions
**Solutions:**
```
1. Ask Claude to cite sources
2. Request confidence levels
3. Ask for caveats and limitations
4. Provide factual context
5. Ask "What don't you know?"
```
**Example Fix:**
```
❌ Before: "What will happen to the market next year?"
✅ After: "Based on current market data, what are 3 possible
scenarios for next year? For each, explain your reasoning and
note your confidence level (high/medium/low)."
```
---
### Issue 3: Vague or Unhelpful Responses
**Symptoms:**
- Generic answers
- Lacks specificity
- Doesn't address the real question
- Too high-level
**Root Causes:**
- Vague prompt
- Missing context
- Unclear objective
- No format specification
**Solutions:**
```
1. Be more specific in the prompt
2. Provide relevant context
3. Specify desired output format
4. Give examples of good responses
5. Define success criteria
```
**Example Fix:**
```
❌ Before: "How can I improve my business?"
✅ After: "I run a SaaS company with $2M ARR. We're losing
customers to competitors. What are 3 specific strategies to
improve retention? For each, explain implementation steps and
expected impact."
```
---
### Issue 4: Too Long or Too Short Responses
**Symptoms:**
- Response is too verbose
- Response is too brief
- Doesn't match expectations
- Wastes tokens
**Root Causes:**
- No length specification
- Unclear scope
- Missing format guidance
- Ambiguous detail level
**Solutions:**
```
1. Specify word/sentence count
2. Define scope clearly
3. Use format templates
4. Provide examples
5. Request specific detail level
```
**Example Fix:**
```
❌ Before: "Explain machine learning"
✅ After: "Explain machine learning in 2-3 paragraphs for
someone with no technical background. Focus on practical
applications, not theory."
```
---
### Issue 5: Wrong Output Format
**Symptoms:**
- Output format doesn't match needs
- Can't parse the response
- Incompatible with downstream tools
- Requires manual reformatting
**Root Causes:**
- No format specification
- Ambiguous format request
- Format not clearly demonstrated
- Missing examples
**Solutions:**
```
1. Specify exact format (JSON, CSV, table, etc.)
2. Provide format examples
3. Use XML tags for structure
4. Request specific fields
5. Show before/after examples
```
**Example Fix:**
```
❌ Before: "List the top 5 products"
✅ After: "List the top 5 products in JSON format:
{
\"products\": [
{\"name\": \"...\", \"revenue\": \"...\", \"growth\": \"...\"}
]
}"
```
---
### Issue 6: Claude Refuses to Respond
**Symptoms:**
- "I can't help with that"
- Declines to answer
- Suggests alternatives
- Seems overly cautious
**Root Causes:**
- Prompt seems harmful
- Ambiguous intent
- Sensitive topic
- Unclear legitimate use case
**Solutions:**
```
1. Clarify legitimate purpose
2. Reframe the question
3. Provide context
4. Explain why you need this
5. Ask for general guidance instead
```
**Example Fix:**
```
❌ Before: "How do I manipulate people?"
✅ After: "I'm writing a novel with a manipulative character.
How would a psychologist describe manipulation tactics?
What are the psychological mechanisms involved?"
```
---
### Issue 7: Prompt is Too Long
**Symptoms:**
- Exceeds context window
- Slow responses
- High token usage
- Expensive to run
**Root Causes:**
- Unnecessary context
- Redundant information
- Too many examples
- Verbose instructions
**Solutions:**
```
1. Remove unnecessary context
2. Consolidate similar points
3. Use references instead of full text
4. Reduce number of examples
5. Use progressive disclosure
```
**Example Fix:**
```
❌ Before: [5000 word prompt with full documentation]
✅ After: [500 word prompt with links to detailed docs]
"See REFERENCE.md for detailed specifications"
```
---
### Issue 8: Prompt Doesn't Generalize
**Symptoms:**
- Works for one case, fails for others
- Brittle to input variations
- Breaks with different data
- Not reusable
**Root Causes:**
- Too specific to one example
- Hardcoded values
- Assumes specific format
- Lacks flexibility
**Solutions:**
```
1. Use variables instead of hardcoded values
2. Handle multiple input formats
3. Add error handling
4. Test with diverse inputs
5. Build in flexibility
```
**Example Fix:**
```
❌ Before: "Analyze this Q3 sales data..."
✅ After: "Analyze this [PERIOD] [METRIC] data.
Handle various formats: CSV, JSON, or table.
If format is unclear, ask for clarification."
```
---
## Debugging Workflow
### Step 1: Identify the Problem
- What's not working?
- How does it fail?
- What's the impact?
### Step 2: Analyze the Prompt
- Is the objective clear?
- Are instructions specific?
- Is context sufficient?
- Is format specified?
### Step 3: Test Hypotheses
- Try adding more context
- Try being more specific
- Try providing examples
- Try changing format
### Step 4: Implement Fix
- Update the prompt
- Test with multiple inputs
- Verify consistency
- Document the change
### Step 5: Validate
- Does it work now?
- Does it generalize?
- Is it efficient?
- Is it maintainable?
---
## Quick Reference: Common Fixes
| Problem | Quick Fix |
|---------|-----------|
| Inconsistent | Add format specification + examples |
| Hallucinations | Ask for sources + confidence levels |
| Vague | Add specific details + examples |
| Too long | Specify word count + format |
| Wrong format | Show exact format example |
| Refuses | Clarify legitimate purpose |
| Too long prompt | Remove unnecessary context |
| Doesn't generalize | Use variables + handle variations |
---
## Testing Checklist
Before deploying a prompt, verify:
- [ ] Objective is crystal clear
- [ ] Instructions are specific
- [ ] Format is specified
- [ ] Examples are provided
- [ ] Edge cases are handled
- [ ] Works with multiple inputs
- [ ] Output is consistent
- [ ] Tokens are optimized
- [ ] Error handling is clear
- [ ] Documentation is complete