In domain-specific applications, GPT-4, augmented with precise prompts or Retrieval-Augmented Generation (RAG), shows notable potential but faces the critical tri-lemma of performance, cost, and data privacy. High performance requires sophisticated processing techniques, yet managing multiple agents within a complex workflow often proves costly and challenging. To address this, we introduce the PEER (Plan, Execute, Express, Review) multi-agent framework. This systematizes domain-specific tasks by integrating precise question decomposition, advanced information retrieval, comprehensive summarization, and rigorous self-assessment. Given the concerns of cost and data privacy, enterprises are shifting from proprietary models like GPT-4 to custom models, striking a balance between cost, security, and performance. We developed industrial practices leveraging online data and user feedback for efficient model tuning. This study provides best practice guidelines for applying multi-agent systems in domain-specific problem-solving and implementing effective agent tuning strategies. Our empirical studies, particularly in the financial question-answering domain, demonstrate that our approach achieves 95.0% of GPT-4's performance, while effectively managing costs and ensuring data privacy.

我们介绍了PEER（计划、执行、表达、审查）多代理框架，通过集成精确的问题分解、高级信息检索、全面的摘要和严格的自我评估，系统化地处理领域特定任务。通过使用在线数据和用户反馈进行高效的模型调优，我们开发了利用网络数据的工业实践，并提供了应用多代理系统解决领域特定问题和实施有效代理调优策略的最佳实践指南。我们的经验证明，特别是在金融问答领域，我们的方法实现了GPT-4性能的95.0％，同时有效管理成本并确保数据隐私。

PEER：使用多智能体框架和调整方法专业化领域特定任务