This course covers building generative AI chat applications. It compares chatbots to generative AI apps, details building methods (APIs, SDKs), user experience enhancements (clarification, context, personalization, accessibility), performance metrics (response time, accuracy, user feedback), and responsible AI principles (trust, safety, data protection). This segment details crucial aspects of improving user experience in AI-powered chat applications. It highlights the importance of features like clarification requests for ambiguous answers, context retention for building upon past interactions, and personalization to tailor responses for individual users, ultimately leading to a more intuitive and satisfying user experience. Key Differences between Chatbots and Generative AI Chat Applications: Chatbots follow predefined scripts, while generative AI applications create contextually relevant responses in real-time. Building Chat Applications: Leverage APIs and SDKs to reduce development time and utilize existing functionalities. Focus on user experience (UX) enhancements. Enhancing User Experience (UX): Prioritize features like clarification requests, context retention, personalization, and accessibility (visual, auditory, motor, cognitive). Fine-tuning Models: Use fine-tuning with pre-trained models and custom datasets to improve performance in specialized domains (e.g., company jargon, medical contexts). This involves selecting a base model, training data, hyperparameters (like epochs), and then training the model. Performance Metrics: Track response time, user satisfaction, accuracy (precision, recall, F1 score), error rate, and anomaly detection to ensure application performance. Responsible AI: Adhere to principles of trust, inclusivity, harm prevention, data protection, and providing mechanisms for improvement and correction of errors.
