- How do we balance the need for domain expertise in critical applications (e.g., kidney dialysis) with the requirement of providing a user-friendly interface that’s accessible to non-experts? What strategies can be employed to design an application that caters to both extremes, from life-critical domains to more general customer service scenarios?
- Can you elaborate on the concept of “tooling around observability” for these types of use cases? How do existing monitoring tools and ML pipelines help in tracking user queries and improving context relevance? What specific metrics or KPIs should be used to measure the success of a model in generating relevant questions, especially when dealing with varying levels of domain expertise?
- How can we incorporate feedback mechanisms into our system to continuously improve the performance of our models and adapt to changing user queries and contexts?
- What role do you see natural language processing (NLP) and machine learning (ML) playing in developing more effective question-answering systems that can handle diverse domains and use cases?
