LLM Integration Patterns for Production Apps

Integrating large language models into production applications requires careful consideration of architecture, reliability, and cost. Here are the patterns we've found most effective.

The Proxy Pattern: Route all LLM calls through a central service that handles caching, rate limiting, fallbacks, and observability. This gives you a single point of control.

The RAG Pattern: Combine your knowledge base with LLM capabilities through Retrieval-Augmented Generation. This keeps responses grounded in your data while leveraging LLM reasoning.

The Chain Pattern: Break complex tasks into chains of simpler prompts. Each step focuses on one thing, making the system more reliable and debuggable.

The Fallback Pattern: Always have fallbacks—whether to a simpler model, cached responses, or graceful degradation. LLM APIs can be unreliable.