This article presents a comprehensive architecture for a multi-agent RAG system that goes beyond basic retrieval-augmented generation. The system features multiple specialized agents that coordinate to handle complex queries, with a built-in evaluation module that assesses response quality. A key innovation is the human-in-the-loop feedback mechanism, where human reviewers can provide feedback on agent outputs, which is then used to automatically refine prompts and improve future responses. The architecture includes components for agent orchestration, context management, and a feedback loop that closes the gap between system outputs and user expectations. For engineering teams, this provides a practical blueprint for building RAG systems that can continuously improve without manual prompt engineering. The evaluation framework covers metrics like relevance, accuracy, and completeness, making it suitable for production deployments where quality assurance is critical.
A detailed architecture for a multi-agent RAG system with self-improvement through evaluation and human-in-the-loop prompt feedback.