Implementing LLM Integration Patterns in Production Systems

Large Language Models have transformed how we build applications. But integrating LLMs into production systems requires careful consideration of latency, cost, reliability, and data privacy. This article covers the patterns we use to integrate LLMs safely and effectively.

The Prompt Layer Pattern

Never embed prompts directly in your application code. Create a dedicated prompt layer that manages templates, versioning, and A/B testing. This allows you to iterate on prompts without redeploying your application, and to track which prompt versions produce the best results.

Fallback and Degradation

LLM APIs can fail, rate-limit, or return unexpected responses. Design your system to degrade gracefully. Implement fallback responses, cached results, and alternative models. Your application should remain functional even when the LLM is unavailable.

Cost Management

LLM costs can spiral quickly. Implement token counting, request caching, and response streaming to control expenses. Use smaller models for simple tasks and reserve expensive models for complex reasoning. Monitor your cost per request and set budget alerts.

Data Privacy

Never send sensitive data to external LLM APIs without proper sanitization. Implement data classification, PII detection, and on-prem model options for regulated workloads. Your users trust you with their data; do not compromise that trust for convenience.