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Mo
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Turning Conversations into Workflows: A Framework to Extract and Evaluate Dialog Workflows for Service AI Agents Key Insight: The paper introduces a novel method using a QA-based chain-of-thought (QA-CoT) prompting framework that automatically extracts structured dialog workflows from historical customer service interactions. Commercial Relevance: For AI product developers and customer service automation platforms, this insight offers a way to reduce manual workflow design and maintenance. Automated extraction of precise and consistent dialog workflows can speed up integration, enhance agent performance, and reduce costs—making customer service bots more robust and scalable. These methods enable fast, reliable, and scalable creation and evaluation of dialog workflows, ensuring service agents can mimic human-like decision processes and maintain high consistency in customer support tasks.
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Key techniques and methods: • Procedural Element-Based Retrieval: – Extracting key procedural details (e.g., intents, slot values, resolution steps) from conversations. – Selecting the most representative interactions to filter noise and ensure accurate workflow extraction. • QA-Based Chain-of-Thought (QA-CoT) Prompting: – Simulating a structured exchange between a “Guide” (asking targeted questions) and an “Implementer” (providing detailed step responses). – Focusing on critical decision points, preconditions, and logical dependencies to produce comprehensive workflows. • End-to-End Evaluation Framework: – Automating the assessment of generated workflows by simulating conversations between customer and service agent bots. – Measuring success through comparison against ground-truth workflows, closely aligning with human assessments.
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