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AI at Work — Certified AI Practitioner
Turn AI literacy into a working professional skill. Six exam-blueprint domains covering prompting mastery, writing, research, documents and data, model selection across ChatGPT, Claude, and Gemini, and safe workplace use — with a Tri-Model Lab in every domain. Practitioner level; builds on AI Foundations.
6 domains / modules
52 lessons
106 practice questions
0.00 USD
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What you'll study
Domain 1 — Prompting Mastery (20%) Module 1.1 — From Requests to Briefs: How Professionals Prompt Module 1.2 — Role, Audience & Voice: Framing the Assistant Module 1.3 — Context Is the Prompt: What to Include, What to Leave Out Module 1.4 — Structured Outputs: Tables, Templates & Formats on Demand Module 1.5 — Show, Don't Tell: Steering with Examples Module 1.6 — Decomposing Big Asks into Chained Steps Module 1.7 — Iteration Patterns: Critique, Refine, Re-run Module 1.8 — Reusable Prompt Templates for Your Job Module 1.9 — When Prompts Fail: Diagnosing Bad Output 🧪 Tri-Model Lab 1 — One Brief, Three Assistants Domain 2 — Writing & Communication (15%) Module 2.1 — Drafting with AI Without Sounding Like AI Module 2.2 — Editing & Rewriting: Tone, Length, Clarity Module 2.3 — Matching Your Own Voice: Style Samples & Guides Module 2.4 — Email Workflows: Triage, Replies, Difficult Messages Module 2.5 — Proposals, Reports & Long-Form Documents Module 2.6 — Summarizing Threads, Meetings & Transcripts Module 2.7 — Translation, Localization & Plain Language 🧪 Tri-Model Lab 2 — The Same Draft, Three Editors Domain 3 — Research & Analysis (15%) Module 3.1 — AI-Assisted Research: Where It Shines, Where It Lies Module 3.2 — Deep Research Modes & Web-Connected Answers Module 3.3 — Source-Checking: Make the Assistant Show Its Work Module 3.4 — Synthesizing Across Multiple Documents Module 3.5 — Structured Comparisons & Decision Matrices Module 3.6 — Numbers & Statistics: The Limits of LLM Math Module 3.7 — Building a Verified Research Workflow 🧪 Tri-Model Lab 3 — One Research Question, Three Answers Domain 4 — Documents, Data & Files (15%) Module 4.1 — PDFs: Extraction, Review & Q&A Module 4.2 — Spreadsheets & CSVs: Cleaning, Formulas, Analysis Module 4.3 — Long-Document Review: Contracts, Policies, Manuals Module 4.4 — Slides & Outlines: From Notes to Deck Module 4.5 — Images & Screenshots as Inputs Module 4.6 — Multi-File Projects: Context Across a Folder Module 4.7 — File Limits & Workarounds 🧪 Tri-Model Lab 4 — One Messy File, Three Cleanups Domain 5 — Picking the Right Model (20%) Module 5.1 — Why Model Choice Matters (and When It Doesn't) Module 5.2 — ChatGPT in Depth: Ecosystem, Strengths, Blind Spots Module 5.3 — Claude in Depth: Long Context, Writing, Documents Module 5.4 — Gemini in Depth: Live Search & the Google World Module 5.5 — Free vs. Paid Tiers: What You Actually Get Module 5.6 — The Task-Fit Decision Framework Module 5.7 — Speed vs. Depth: Reasoning Modes Module 5.8 — Cross-Checking: A Second Model as Reviewer Module 5.9 — Keeping Your Map Current as Models Change 🧪 Tri-Model Lab 5 — Routing a Week of Real Tasks Domain 6 — Privacy, Security & Workplace Policy (15%) Module 6.1 — What Never Goes in a Prompt Module 6.2 — How Your Data Is Used: Training, Retention, Settings Module 6.3 — Client & Employer Obligations: NDAs, Contracts, AI Module 6.4 — Workplace AI Policies: Reading Yours, Writing One Module 6.5 — Prompt Injection & Manipulated Content: A User's Defense Module 6.6 — Verification Duty & Disclosure: Owning AI Output Module 6.7 — Shadow AI: The Risk of Unapproved Tools 🧪 Tri-Model Lab 6 — Privacy Settings Walkthrough, All Three Related courses
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