LLM Integration Patterns: Best Practices from Production

LLM Integration Patterns

Integrating LLMs into production is challenging. After processing millions of AI requests, we have learned what works and what does not. Here are the patterns we use at Afto to build reliable AI-powered automation.

Pattern 1 - Prompt Templates: Create reusable prompt templates with variables, version control your prompts, A/B test prompt variations, monitor prompt performance. We maintain a library of 50+ tested prompts for common tasks like data extraction, content generation, classification, and summarization.

Pattern 2 - Structured Output: Use JSON mode for structured data, validate outputs with Zod schemas, retry with corrections on validation failure, fallback to simpler models if needed. Result: 95 percent success rate on first attempt.

Pattern 3 - Context Management: Implement sliding window for long conversations, summarize old context to save tokens, use vector search for relevant context retrieval, separate system prompts from user context. This reduces costs by 50 percent while maintaining quality.

Pattern 4 - Error Handling: Implement exponential backoff for rate limits, graceful degradation when AI fails, human-in-the-loop for critical decisions, comprehensive logging for debugging. Our error rate is below 0.1 percent. Want to build AI-powered automation? Start at /contact