This module equips learners with the knowledge and techniques to conduct effective user research, focusing on designing interviews, analyzing conversational data, and interpreting survey responses to improve AI applications like ChatGPT. It covers how to gather, organize, and derive meaningful insights from user interactions.

This module explores how to identify and prioritize use cases for ChatGPT by aligning them with user goals, evaluating their value, and understanding limitations. Learners will gain practical insights into applying generative AI effectively in real-world scenarios and contrasting it with traditional methods.

This module teaches learners how to prioritize backlog items using the WSJF and user needs scoring (UNS) methods. It covers techniques for consistent scoring, understanding frequency, and applying these approaches to real-world development scenarios. Learners will gain practical skills in aligning development efforts with customer value and cost efficiency.

This module covers the essential principles of designing inclusive and user-friendly digital experiences, focusing on chat interfaces, accessibility, internationalization, and responsive design. Learners will gain insights into how to prioritize user needs, structure interactive elements, and ensure global usability across different platforms and languages.

This module explores the process of gathering, preparing, and integrating enterprise data into large language models using RAG techniques. Learners will gain practical insights into data quality, cleaning, annotation, and security considerations, while also examining real-world case studies and ethical implications.

This module covers the fundamentals of prompt engineering, including strategies for crafting effective prompts in enterprise settings, techniques for improving model responses, and methods for evaluating and refining prompt effectiveness. Learners will gain practical skills in designing instructions, using frameworks like RACE and CO-STAR, and leveraging examples to enhance model performance.

This module covers the process of fine-tuning models to improve performance when prompt engineering is no longer sufficient. Learners will explore how to create, test, and apply fine-tuned models for structured outputs and tool integration. The module also includes a case study on data cleansing and model optimization.

This module explores conversational guidelines and heuristics to improve user experience in chat-based interfaces. It covers how to design effective communication strategies, handle errors, and create consistent tone and style. Learners will gain practical skills in evaluating and refining conversational systems.

This module provides an in-depth look at evaluating and monitoring AI systems, focusing on key metrics like Faithfulness, Context Recall, and User Experience (UX) scores. Learners will gain an understanding of how to systematically assess AI performance and implement reliable testing strategies.

This module explores the integration of design thinking into AI development and the creation of efficient content improvement life cycles. It covers key aspects such as team dynamics, Agile methodologies, and the importance of user research and data analysis in AI systems. Learners will gain practical insights into managing AI development processes effectively.

This module guides learners through the process of applying AI principles to real-world challenges, emphasizing the importance of data understanding, critical thinking, and customer-centric AI solutions. It covers how to design accountability systems and tailor AI tools to user needs, ensuring effective and responsible AI implementation.