This article presents a systematic approach to orchestrating multiple large language models through API integration, focusing on prompt engineering and multi-model collaboration. The author outlines a practical engineering scheme that goes beyond simple single-model usage, addressing challenges like context management, response aggregation, and error handling across different LLM providers. This reflects a broader trend in China's AI development community toward building production-grade multi-model pipelines. For overseas developers, the key takeaway is the architectural pattern: using a central orchestrator to route prompts, manage state, and combine outputs from models like GPT, Claude, and domestic alternatives. The approach is particularly relevant for teams building complex AI workflows that require specialized model capabilities for different subtasks. While the specific code examples are platform-specific, the engineering principles—modular design, fallback strategies, and prompt templating—are universally applicable.
A practical guide to orchestrating multiple LLMs via API, combining prompt engineering with multi-model collaboration for production systems.