Compound AI Systems
AI applications that combine multiple models, retrievers, and tools to solve complex tasks.
Definition
Compound AI systems are applications that integrate multiple AI components—language models, retrievers, specialized models, tools, and code—working together to accomplish tasks beyond what any single model could achieve. Rather than relying on one monolithic model, these systems orchestrate various components strategically.
This architectural pattern has emerged as the dominant approach for production AI applications, enabling better performance, cost optimization, and maintainability compared to single-model solutions.
Why It Matters
The best AI applications aren't built on a single model—they combine multiple components strategically. Understanding compound systems helps you architect AI solutions that are more capable, reliable, and cost-effective.
For businesses, this means building AI that plays to the strengths of different models and tools rather than expecting one solution to do everything.
Examples in Practice
A document processing pipeline uses a fast classifier to route documents, specialized extractors for different document types, and a language model for summarization.
A customer service system combines a retriever for knowledge lookup, a small model for intent classification, and a large model for response generation.
An analytics platform uses vision models for chart understanding, language models for insight generation, and code execution for calculations.