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Continuous Improvement of Conversational AI in Production | Rasa Summit

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Continuous Improvement of Conversational AI in Production | Rasa Summit

  1. 1. Continuous Improvement of Conversational AI 
 In Production Jielei LI Cognitive software engineer for Djingo ▪ Why important ▪ How the process works ▪ Lessons learned
  2. 2. Interne Orange Challenge of handling infinite spontaneous responses. Continuous learning cycle based on real-user conversations and feedback can be extremely helpful. Difficult to plan for every eventuality Chatbot projects learn by doing. Continuous Improvement
  3. 3. Interne Orange Continuous Improvement Process User feedback Analyze Improve Test Deploy Measure Effectiveness Create / Update Training Data Analyze Training Test Data Pre-Deployment Testing
  4. 4. Collect feedback Macro KPI Micro vision Task completion, NPS, Human takeover rate… Specific needs of every project NLU : Analyze the Quality of Intent and Entity Detection Conversation : Investigate user experiences, when users abandon the dialog Measure and Improve Effectiveness Identify weak spots Improve Fix Existing Training Adapt to the conversation flow
  5. 5. Interne Orange 2 1 Reading real conversations is critical to improving the bot. 3 Good tools boost productivity. Define KPI to better prioritize future tasks. 4 We will need people with different skill sets. Developers Linguists Domain experts Ux designers Product managers Devops Lessons learned Conversations K P I S k i l l s e t s Tooling
  6. 6. Thanks for your attention Jielei LI

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