In this module, you will learn the core concepts behind building custom GPTs, including how to shape a GPT’s behavior through instructions, add relevant external knowledge using retrieval-augmented generation (RAG), and bring these elements together into a more capable and personalized AI assistant. You will also explore how tools and actions expand what a custom GPT can do, and how frameworks such as CAPITAL, prompt patterns, and persona design can be used to shape its tone, behavior, and overall user experience.

In this module, you will learn how to make custom GPTs more reliable, useful, and human-centered by testing them systematically and designing better interaction patterns. You will explore how to build simple benchmarks and create meaningful test cases, including expected, ambiguous, and high-risk scenarios, to evaluate GPT performance over time. You will also examine techniques such as direct quotations, output templates, the Flipped Interaction Pattern, menu-based navigation, and feature toggles to structure responses, gather the right context, and guide users more effectively. Throughout the module, the focus is on building GPTs that support human reasoning, surface ambiguity, and direct users toward the right evidence, options, or human support when needed.

In this module, you will learn how to make custom GPTs safer and more reliable when users ask unclear, ambiguous, or risky questions. You will explore how to define boundaries and ‘escape valves’ so a GPT knows when not to answer directly, how to handle ambiguity, conflicting information, or gaps in knowledge sources and user prompts, and how to guide users more effectively through prompt patterns such as question refinement, alternative approaches, and cognitive verification. By the end of the module, you will understand how to design custom GPTs that remain helpful while reducing the risk of misleading, overconfident, or unsupported responses.