Practical AI at Work: Workflows, Safety, and Verification
A practical, introductory course designed to help professionals confidently integrate generative AI into their daily workflows. Learners will master safe data practices, efficient task chaining, and critical verification skills to use AI as a secure and effective co-pilot.
What Generative AI Can (and Can't) Do Define generative AI in the context of daily office tasks without using technical jargon. Identify at least three common workplace scenarios where AI excels. Recognize the primary limitations of AI, including lack of context and reasoning. The Human-in-the-Loop Principle Explain the concept of 'human-in-the-loop' workflows. Differentiate between delegating a task to AI and abdicating responsibility. Determine which types of workplace tasks require high human oversight. The Dangers of Shadow AI Define 'Shadow AI' and explain why it poses a risk to organizational security. Identify the differences between public, unapproved AI tools and secure, enterprise-approved tools. Recognize the consequences of data leakage through AI platforms. Setting Safe Data Boundaries Categorize workplace data into 'safe to share' and 'confidential'. Apply a basic sanitization framework to remove sensitive information from prompts. Evaluate situational scenarios to determine if data is safe to upload. Task Chaining for Drafting and Research Explain the concept of 'task chaining' to break large projects into manageable AI prompts. Execute a multi-step AI workflow for a writing assignment. Refine AI outputs iteratively by providing continuous feedback and context. AI for Summarization and Basic Analysis Use AI tools to efficiently summarize lengthy documents or meeting transcripts. Extract key themes and action items from large blocks of text. Format AI outputs into usable structures like tables or bulleted lists. Spotting AI Hallucinations Define AI 'hallucinations' and explain why generative tools sometimes fabricate information. Identify common linguistic and structural clues that indicate a potential hallucination. Critically evaluate an AI-generated document to flag subtle errors or inconsistencies. Fact-Checking and Avoiding 'Botsitting' Establish a routine for verifying AI-generated claims against trusted original sources. Identify when fixing a poor AI output takes more time than doing the work manually. Apply strategies to prevent 'botsitting' and maximize actual time saved.