This module explains, in plain language, how generative AI produces responses and why those responses can still be inaccurate, outdated, biased, or unsupported. Through a workplace example involving an unsourced market statistic, learners explore how training, inference, tokens, patterns, and context shape outputs. The module helps learners move beyond trusting fluent answers at face value by building habits of verification, safer rewriting, and source-based decision making.

This module helps learners compare common AI tools and choose a realistic workplace use case that is useful, achievable, and low risk. Working with a small retailer scenario, learners review chat assistants, search-grounded tools, document analysis, spreadsheet assistance, multimodal features, and a value-effort-risk matrix. The focus is on practical prioritization so learners can select a tool thoughtfully, justify expected value, and communicate clear guardrails for responsible use.

This module focuses on the safeguards that make workplace AI use more secure, responsible, and inclusive. Through scenarios involving confidential information, organizational policy, consent, fairness, copyright, traceability, and human review, learners practice spotting risks before they become problems. By the end of the module, learners will be better prepared to protect sensitive information, redesign unsafe requests, and apply review checkpoints that support trustworthy AI-assisted work.

This module brings the course together by guiding learners to design a practical AI-assisted workflow for routine work. Using examples such as a repeatable weekly briefing, learners break tasks into steps, define input-output expectations, set verification and acceptance criteria, and decide when human escalation is necessary. The goal is to help learners create a lightweight process that improves efficiency while protecting quality, accountability, and responsible decision making.