LLM Integration¶

A comprehensive guide to integrating Large Language Models (LLMs) into applications.

Introduction¶

Effective LLM integration requires careful consideration of architecture, performance, cost, and user experience. This guide covers key patterns and best practices.

Integration Patterns¶

Direct Integration¶

• API-based integration
• Self-hosted models
• Batch processing
• Streaming responses

RAG Integration¶

• Document processing
• Vector storage
• Query processing
• Response generation

Fine-tuning¶

• Dataset preparation
• Training strategies
• Model evaluation
• Deployment considerations

Best Practices¶

Prompt Engineering¶

• Prompt design patterns
• System messages
• Few-shot learning
• Output formatting

Performance Optimization¶

• Caching strategies
• Batch processing
• Response streaming
• Load balancing

Cost Management¶

• Token optimization
• Model selection
• Caching policies
• Usage monitoring

Error Handling¶

• Rate limiting
• Fallback strategies
• Retry mechanisms
• Monitoring and alerts

Security Considerations¶

Data Privacy¶

• Input sanitization
• Output filtering
• PII handling
• Audit logging

Model Security¶

• Access control
• API key management
• Request validation
• Response filtering

Monitoring and Analytics¶

Key Metrics¶

• Response times
• Token usage
• Error rates
• Cost per request

Quality Assurance¶

• Output validation
• Semantic accuracy
• Consistency checks
• User feedback