Start Course 1 with Lesson 1, then save your first result.

Outcome

Choose the right AI task, avoid unsafe inputs, and verify outputs before using them.

Best for: New AI users, office workers, students, small business owners

Core lessons

1. What AI tools can and cannot do
2. Sensitive data, illegal-use boundaries, and safe task selection
3. How to check AI answers for facts, bias, privacy, and tone
4. Daily practice: summarize, rewrite, compare, plan, and verify

Guided lab pages

• Choose safe AI tasks
• Rewrite a risky request
• Verify before using output

Proof standard

Create a personal AI safety checklist and use it on one real work task.

• Safety
• Accuracy
• Privacy
• Usefulness

Outcome

Turn weak asks into reusable prompts with clear goals, context, examples, and output format.

Best for: Anyone who wants better AI answers from ChatGPT, Gemini, Claude, or Copilot

Core lessons

1. Weak prompt vs strong prompt
2. Goal, context, source material, format, tone, and limits
3. Follow-up prompts and answer repair
4. Prompt templates for work, study, business, and content

Guided lab pages

• Upgrade a weak prompt
• Control output format
• Repair a weak answer

Proof standard

Use AI Studio to improve one real prompt and save the stronger version.

• Clarity
• Context
• Output format
• Repeatability

Outcome

Understand when to use each assistant and how to control memory, files, sources, and quality.

Best for: Users choosing which AI assistant to use for real tasks

Core lessons

1. ChatGPT-style assistants for flexible writing, planning, coding, and multimodal work
2. Claude-style assistants for long documents, reasoning, and careful rewriting
3. Gemini and Google workflows for search, docs, sheets, and workspace tasks
4. Copilot for Microsoft 365 documents, email, meetings, and Teams workflows

Guided lab pages

• Pick the right assistant
• Compare outputs fairly
• Use files safely

Proof standard

Build a one-page guide for which AI assistant you should use for common tasks.

• Tool fit
• Privacy
• Source handling
• Output quality

Outcome

Use AI for research without blindly trusting unsupported claims.

Best for: Students, business owners, analysts, marketers, and professionals

Core lessons

1. How to ask for sourced research and assumptions
2. Perplexity, browsing, Gemini, and NotebookLM-style workflows
3. Claim logs, source quality, dates, and contradiction checks
4. Turning research into a decision memo

Guided lab pages

• Build a claims log
• Turn research into a decision memo
• Detect weak sources

Proof standard

Produce a one-page research brief with at least five checked claims.

• Source quality
• Recency
• Balanced reasoning
• Risk notes

Outcome

Use AI to handle emails, meeting notes, SOPs, summaries, and reports faster.

Best for: Admin, customer service, operations, managers, and coordinators

Core lessons

1. Professional email drafts and replies
2. Meeting notes, action items, and follow-up messages
3. SOP and checklist creation
4. Reports, summaries, and decision notes

Guided lab pages

• Rough notes to professional email
• Meeting notes to action plan
• Messy process to SOP

Proof standard

Build one reusable work template and review it for accuracy and tone.

• Professional tone
• Completeness
• Action clarity
• Verification

Outcome

Use AI inside everyday office tools without losing control of the final work.

Best for: People who use Gmail, Docs, Sheets, Slides, Outlook, Word, Excel, PowerPoint, or Teams

Core lessons

1. Gmail, Docs, Sheets, Slides, and Meet workflows
2. Outlook, Word, Excel, PowerPoint, and Teams workflows
3. How to prepare context before asking an assistant
4. How to review AI-created documents before sharing

Guided lab pages

• Gmail or Outlook reply workflow
• Docs or Word briefing workflow
• Slides or PowerPoint outline

Proof standard

Create a prompt pack for the office suite you use most.

• Context quality
• Tool fit
• Editability
• Final review

Outcome

Use AI to understand data, explain formulas, summarize tables, and check numbers.

Best for: Workers who use Excel, Google Sheets, reports, or business data

Core lessons

1. How to describe a dataset safely
2. Formula explanation and spreadsheet cleanup
3. Table summaries, trends, outliers, and caveats
4. How to avoid trusting wrong calculations

Guided lab pages

• Ask better data questions
• Explain and test formulas
• Build a dashboard plan

Proof standard

Create a data question, AI analysis prompt, and verification checklist.

• Data clarity
• Formula logic
• Manual verification
• Business usefulness

Outcome

Create useful campaigns, captions, hooks, ad ideas, and customer messages without fake claims.

Best for: Small businesses, creators, marketers, and sales teams

Core lessons

1. Audience, offer, pain point, and proof
2. Hook, caption, CTA, and hashtag workflow
3. Ad claim safety and compliance review
4. Sales follow-up and objection handling

Guided lab pages

• Audience and offer brief
• 7-day content calendar
• Sales follow-up without pressure

Proof standard

Create one safe social post package with caption, image brief, and CTA.

• Audience fit
• Clarity
• Claim safety
• CTA strength

Outcome

Write clear briefs for Canva, Firefly, Midjourney, ChatGPT Images, thumbnails, and brand visuals.

Best for: Creators, marketers, small businesses, and non-designers

Core lessons

1. Image prompt structure: subject, scene, style, composition, and constraints
2. Brand-safe visual direction
3. Thumbnail, ad, and social post briefs
4. Copyright, identity, and misleading-image boundaries

Guided lab pages

• Write a usable image brief
• Thumbnail concept review
• Brand safety check

Proof standard

Turn one topic into a thumbnail brief, image brief, and revision checklist.

• Visual clarity
• Brand fit
• Specificity
• Safety

Outcome

Plan short videos, voiceovers, captions, storyboards, and slide decks with AI support.

Best for: Creators, trainers, business owners, educators, and marketers

Core lessons

1. Short video structure: hook, problem, teaching point, CTA
2. Voiceover and caption workflows
3. Presentation outlines and slide copy
4. Reviewing generated media for accuracy and brand fit

Guided lab pages

• 30-second teaching script
• Voiceover and caption plan
• Presentation from topic

Proof standard

Create a script, caption, voiceover direction, and slide outline from one topic.

• Story flow
• Audience fit
• Clarity
• Review quality

Outcome

Decide when to use prompts, templates, automations, or agents with human approval steps.

Best for: Operators, founders, admins, and technical beginners

Core lessons

1. Prompt vs template vs automation vs agent
2. Triggers, actions, approvals, and logging
3. Lead follow-up, support triage, content scheduling, and reports
4. Cost, privacy, and abuse controls

Guided lab pages

• Map a repeated task
• Estimate cost and risk
• Create an approval policy

Proof standard

Design one automation map with trigger, AI step, review step, and final action.

• Workflow logic
• Safety gates
• Cost control
• Maintainability

Outcome

Turn repeated work into repeatable systems that can be delegated, measured, and improved.

Best for: Small business owners, team leads, operations staff, and consultants

Core lessons

1. How to find repeatable work worth systemizing
2. Writing SOPs with AI without missing human judgment
3. Creating intake forms, checklists, and quality controls
4. Updating workflows when results are weak

Guided lab pages

• Find work worth systemizing
• Write an SOP
• Improve the system monthly

Proof standard

Build one complete SOP and review checklist for a real repeated task.

• Repeatability
• Clarity
• Quality control
• Human ownership

Outcome

Turn trusted sources into briefs, FAQs, study guides, onboarding notes, and question-answer workflows without losing source control.

Best for: Students, teams, trainers, researchers, operations staff, and business owners

Core lessons

1. When to use NotebookLM, source-grounded assistants, search AI, or ordinary chat
2. How to prepare source packs, remove sensitive information, and ask grounded questions
3. Citation checks, contradiction checks, source recency, and source-quality notes
4. Turning source collections into onboarding guides, internal FAQs, study notes, and client briefs

Guided lab pages

• Prepare a source pack
• Ask grounded questions
• Turn sources into a brief

Proof standard

Create a source-grounded brief with source notes, confidence levels, and follow-up questions.

• Source control
• Citation discipline
• Privacy
• Usefulness

Outcome

Evaluate AI output with rubrics, examples, source checks, bias checks, and before/after quality notes.

Best for: Managers, creators, analysts, operators, and anyone publishing AI-assisted work

Core lessons

1. What makes an AI answer good enough for work
2. Rubrics, golden examples, edge cases, and comparison tests
3. Hallucination checks, bias/tone review, source verification, and claim safety
4. How to document human edits and decide whether output is ready

Guided lab pages

• Score an AI output
• Create a golden example
• Build a QA checklist

Proof standard

Create an output scorecard that can be reused before sending, publishing, or automating AI work.

• Accuracy
• Consistency
• Risk review
• Edit quality

Outcome

Use AI coding and no-code builders safely for specs, prototypes, bug reports, and handoff notes.

Best for: Non-technical founders, operators, marketers, admins, and learners working with developers

Core lessons

1. When to use ChatGPT, Claude, GitHub Copilot, Codex-style tools, Cursor, Replit, v0, or no-code builders
2. Writing specs, acceptance criteria, sample data, and test cases before asking AI to build
3. Secrets, API keys, privacy, permissions, and why code still needs review
4. How to hand off an AI-built prototype to a developer or operator

Guided lab pages

• Write a build-ready spec
• Create test cases
• Prepare a handoff brief

Proof standard

Create a build-ready spec and test checklist without exposing secrets or private data.

• Specification clarity
• Testing
• Security
• Handoff quality

Outcome

Understand agents, permissions, tools, memory, approval gates, logs, evaluations, rollback, and cost limits.

Best for: Team leads, founders, operators, technical beginners, and anyone planning agent workflows

Core lessons

1. Agent vs chatbot vs automation: what changes when AI can take actions
2. Permissions, tools, memory, data access, logs, and human approval
3. Agent examples: Copilot Studio, Zapier Agents, Make AI Agents, Gemini agents, and OpenAI-style agents
4. Cost caps, failure handling, abuse prevention, and when not to use an agent

Guided lab pages

• Choose prompt, automation, or agent
• Design an approval policy
• Set cost and failure limits

Proof standard

Build an agent readiness checklist before connecting any workflow to external actions.

• Governance
• Safety gates
• Cost control
• Operational clarity

Outcome

Measure whether AI is saving time, improving quality, reducing rework, or creating unnecessary risk.

Best for: Founders, managers, teams, consultants, and learners proving practical value

Core lessons

1. Choosing AI use cases by value, frequency, risk, and learning curve
2. Time saved, rework reduced, quality improved, and cost per workflow
3. Team adoption metrics, completion rates, manager review, and support signals
4. When not to automate: low value, high risk, unclear ownership, or weak data

Guided lab pages

• Pick high-value use cases
• Estimate ROI without hype
• Create an adoption dashboard

Proof standard

Create a one-page AI ROI note that explains what improved, what still needs review, and what not to automate.

• Business value
• Measurement
• Risk adjustment
• Decision quality

Outcome

Complete one full workflow with prompts, outputs, verification, limitations, and reflection.

Best for: Learners who want proof that they can use AI for practical work

Core lessons

1. Choose a real workflow worth improving
2. Build prompts, outputs, review checks, and reusable templates
3. Document limitations, risks, and human review steps
4. Prepare a simple portfolio summary

Guided lab pages

• Choose a final project workflow
• Build the portfolio work sample
• Reflect and certify

Proof standard

Create one portfolio-ready AI workflow with evidence and review notes.

• Practical value
• Verification
• Safety
• Reusable workflow