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Build, Judge, Optimize: A Blueprint for Continuous Improvement of Multi-Agent Consumer Assistants
arXiv – CS AI|Alejandro Breen Herrera, Aayush Sheth, Steven G. Xu, Zhucheng Zhan, Charles Wright, Marcus Yearwood, Hongtai Wei, Sudeep Das|
🤖AI Summary
Researchers present a blueprint for evaluating and optimizing multi-agent conversational shopping assistants, addressing challenges in multi-turn interactions and tightly coupled AI systems. The paper introduces evaluation rubrics and two prompt-optimization strategies including a novel Multi-Agent Multi-Turn GEPA approach for system-level optimization.
Key Takeaways
- →Moving conversational shopping assistants from prototype to production reveals significant evaluation and optimization challenges.
- →The research introduces a multi-faceted evaluation rubric that decomposes shopping quality into structured dimensions.
- →A calibrated LLM-as-judge pipeline was developed and aligned with human annotations for evaluation.
- →Two complementary optimization strategies were investigated: Sub-agent GEPA and the novel MAMuT GEPA approach.
- →The team released rubric templates and evaluation design guidance to support practitioners building production systems.
#multi-agent-ai#conversational-ai#llm-optimization#ai-evaluation#shopping-assistants#prompt-optimization#production-ai#gepa
Read Original →via arXiv – CS AI
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