AI Model Selection, Tasks, and Responsible Use Cases By the end of this lesson, students should be able to: AI quality depends on input quality. For prompting, that means clear context. For classifiers, that means useful examples and labels. For retrieval, that means documents that are current, organized, and relevant. For generated media, that means safe style guidance and review rules. Data can fail in several ways. It may be missing key cases, too old, collected from the wrong audience, inconsistent, private, or biased. A workflow that looks smart in a demo can fail badly when real data arrives. Students should build a data readiness note before implementation. It does not need to be fancy. It should answer: What data exists? What is missing? What cannot be used? What examples are needed to test the workflow? Enroll to continue this lesson. The preview above shows the lesson objectives and opening lesson content. Enroll to view the full lesson, complete the practice work, and take the lesson quiz.
Lesson 2: Data Readiness and Workflow Context
Lesson Objectives
Lesson Content
