LLM Integration Patterns: 7 Architectures I've Deployed in Production

Beyond the Basic API Call Most teams start their LLM journey with a simple API call: send a prompt, get a response. That works for prototypes, but production systems need more robust patterns. Here are seven architectures I've deployed at client companies through WEDGE Method's AI consulting practice. 1. Retrieval-Augmented Generation (RAG) Use case: Customer support bot over your docs. Embed the query, vector search for relevant chunks, inject into LLM prompt, generate grounded answers with citations. Key lesson: 500-token chunks with 100-token overlap works best for technical docs. 2. Multi-Agent Orchestrator Use case: Complex business processes. An orchestrator agent coordinates specialized sub-agents: Research Agent, Analysis Agent, Writing Agent, Action Agent. Key lesson: Give each agent a narrow role. Agents that do everything do nothing well. 3. Human-in-the-Loop Processor Use case: Invoice processing where accuracy is critical. AI extracts data with confidence scoring. High-con