Examples

Code Examples

Practical examples and code snippets to help you integrate Prompt Folding™ into your applications. From simple prompts to complex hierarchical structures.

Quick Examples

⚡

Basic Prompt Folding

Python

import prompt_folding

client = prompt_folding.Client(api_key="YOUR_API_KEY")

# Simple prompt optimization
result = client.fold(
    prompt="Explain quantum computing in detail",
    optimization_level="balanced"
)

print(f"Original: {result.original_tokens} tokens")
print(f"Folded: {result.folded_tokens} tokens")
print(f"Reduction: {result.reduction_percentage}%")

JavaScript

import { PromptFolding } from '@prompt-folding/core';

const client = new PromptFolding('YOUR_API_KEY');

// Simple prompt optimization
const result = await client.fold({
  prompt: 'Explain quantum computing in detail',
  optimizationLevel: 'balanced'
});

console.log(`Original: ${result.originalTokens} tokens`);
console.log(`Folded: ${result.foldedTokens} tokens`);
console.log(`Reduction: ${result.reductionPercentage}%`);
🎯

Context-Aware Folding

Python

# Context-aware optimization
result = client.fold(
    prompt="Create a marketing campaign",
    context="Target audience: Tech professionals aged 25-40",
    optimization_level="aggressive",
    preserve_quality=True
)

print(f"Quality score: {result.quality_score}")
print(f"Folded prompt: {result.folded_prompt}")

JavaScript

// Context-aware optimization
const result = await client.fold({
  prompt: 'Create a marketing campaign',
  context: 'Target audience: Tech professionals aged 25-40',
  optimizationLevel: 'aggressive',
  preserveQuality: true
});

console.log(`Quality score: ${result.qualityScore}`);
console.log(`Folded prompt: ${result.foldedPrompt}`);

Use Case Examples

📝

Content Generation

Optimize prompts for blog posts, articles, and marketing content

Blog Post Generation

# Blog post with hierarchical structure
blog_prompt = """
Write a comprehensive blog post about AI trends in 2024.
Include:
- Introduction
- Key trends
- Industry impact
- Future predictions
- Conclusion
"""

result = client.fold(
    prompt=blog_prompt,
    context="Tech blog audience, 1500 words",
    optimization_level="balanced"
)

# Use the folded prompt with your AI model
response = ai_model.generate(result.folded_prompt)

Marketing Copy

# Marketing copy optimization
marketing_prompt = """
Create compelling marketing copy for our AI platform.
Focus on:
- Problem statement
- Solution benefits
- Call to action
- Social proof
"""

result = client.fold(
    prompt=marketing_prompt,
    context="B2B SaaS, enterprise decision makers",
    optimization_level="aggressive",
    target_tokens=200
)

print(f"Optimized for {result.folded_tokens} tokens")
📊

Data Analysis

Optimize prompts for data analysis and reporting tasks

Data Visualization

# Data visualization prompt
viz_prompt = """
Analyze this sales data and create visualizations:
- Monthly revenue trends
- Product performance
- Customer segments
- Geographic distribution

Data: {sales_data}
"""

result = client.fold(
    prompt=viz_prompt,
    context="Business intelligence dashboard",
    optimization_level="balanced"
)

# Generate visualizations with optimized prompt
charts = ai_model.generate_charts(result.folded_prompt)

Report Generation

# Automated report generation
report_prompt = """
Generate a quarterly business report including:
- Executive summary
- Financial highlights
- Key metrics
- Recommendations
- Risk assessment

Data sources: {financial_data}
"""

result = client.fold(
    prompt=report_prompt,
    context="C-level executives, board members",
    optimization_level="minimal",
    preserve_quality=True
)

report = ai_model.generate_report(result.folded_prompt)
💻

Code Generation

Optimize prompts for code generation and software development

API Integration

# API integration code generation
api_prompt = """
Create a Python client for the Prompt Folding API.
Include:
- Authentication
- Error handling
- Rate limiting
- Retry logic
- Type hints
"""

result = client.fold(
    prompt=api_prompt,
    context="Production-ready Python library",
    optimization_level="balanced"
)

# Generate the client code
client_code = ai_model.generate_code(result.folded_prompt)

Testing Framework

# Test suite generation
test_prompt = """
Generate comprehensive tests for a React component.
Include:
- Unit tests
- Integration tests
- Edge cases
- Accessibility tests
- Performance tests

Component: {component_code}
"""

result = client.fold(
    prompt=test_prompt,
    context="Jest + React Testing Library",
    optimization_level="aggressive"
)

test_suite = ai_model.generate_tests(result.folded_prompt)

Advanced Patterns

BATCH

Batch Processing

Process multiple prompts efficiently with batch operations.

Python Batch

# Batch processing multiple prompts
prompts = [
    {"id": "blog_1", "text": "Write about AI trends"},
    {"id": "blog_2", "text": "Explain machine learning"},
    {"id": "blog_3", "text": "Discuss data science"}
]

batch_result = client.fold_batch(
    prompts=prompts,
    optimization_level="balanced"
)

for result in batch_result.results:
    print(f"{result.id}: {result.reduction_percentage}% reduction")

JavaScript Batch

// Batch processing multiple prompts
const prompts = [
  { id: 'blog_1', text: 'Write about AI trends' },
  { id: 'blog_2', text: 'Explain machine learning' },
  { id: 'blog_3', text: 'Discuss data science' }
];

const batchResult = await client.foldBatch({
  prompts,
  optimizationLevel: 'balanced'
});

batchResult.results.forEach(result => {
  console.log(`${result.id}: ${result.reductionPercentage}% reduction`);
});
HIERARCHY

Hierarchical Structures

Create complex hierarchical prompt structures for advanced use cases.

# Hierarchical prompt structure
hierarchical_prompt = """
SYSTEM: You are an expert content strategist.

CONTEXT: 
- Industry: Technology
- Audience: Enterprise decision makers
- Format: Executive summary

STRUCTURE:
1. Problem Analysis
   - Current challenges
   - Market dynamics
   - Competitive landscape

2. Solution Framework
   - Strategic approach
   - Implementation roadmap
   - Success metrics

3. Risk Assessment
   - Potential obstacles
   - Mitigation strategies
   - Contingency plans

TASK: Create a comprehensive strategy document.
"""

result = client.fold(
    prompt=hierarchical_prompt,
    context="Strategic consulting report",
    optimization_level="balanced"
)

# The folded prompt maintains the hierarchical structure
# while reducing token count significantly

Performance Tips

Optimization Strategies

  • ⚡Use balanced optimization for most use cases
  • 🎯Set target_tokens for specific requirements
  • 🔒Enable preserve_quality for critical content
  • 📦Use batch processing for multiple prompts

Best Practices

  • 📝Provide clear context for better optimization
  • 🔄Cache folded prompts for repeated use
  • ⚖️Balance token reduction with quality preservation
  • 📊Monitor quality scores and adjust accordingly

Ready to Start Building?

Get your API key and start optimizing prompts with these examples.

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