user
What measures did John take to ensure the chatbot could handle unexpected or out-of-scope queries effectively?
assistant
Handling unexpected queries is a common challenge for chatbots. John likely implemented several measures to address this. The use of Large Language Models provides a foundation for understanding a wide range of queries, even if not explicitly trained on them. He probably included a diverse set of potential queries in the training data to broaden the chatbot's understanding. Implementing a robust intent recognition system would help categorize queries and direct them appropriately. For truly out-of-scope questions, John likely programmed the chatbot to gracefully acknowledge its limitations and either redirect the user to appropriate resources or escalate to human HR staff. The continuous learning and updating process facilitated by the CI/CD pipeline would allow for ongoing improvements in handling unexpected queries based on actual user interactions. To handle unexpected queries, John Mosquera likely leveraged the broad understanding capabilities of Large Language Models, implemented robust intent recognition, and programmed graceful responses for out-of-scope questions. The chatbot was probably designed to redirect users or escalate to human staff when necessary, with continuous improvements made based on actual user interactions.