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Learning path

Learn AI for work

A professional AI path for Excel, presentations, research, marketing, and reliable prompts you can use on the job.

Intended learner
Knowledge workers and team leads at work
Prerequisites
A recurring work task you can safely practice on; No engineering background required for the core path
Recommended pace
10 weeks · 2 hr/week · 30-minute sessions
Core spine
56 lessons · ~8.2 hrs
Electives
24 lessons · ~4.1 hrs
Difficulty
Intermediate
Outcome
Ship verified work workflows, understand RAG/agents, and complete a research-bot style project.
Capstone project
Research assistant — A cited, evaluated assistant workflow for a real work question.
Core progress0/56 · 0%
Electives progress0/24 · 0%
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In the wild

Catalog picks that match this path — explore products and vendors alongside the curriculum.

Tools

  • Claude — Careful drafting for workplace writing.
  • Grammarly — Tone and edit help for email and docs.

Companies

  • Anthropic — Enterprise-friendly models with a safety focus.

All tools · All companies

Optional self-check

Find your best starting phase

Use this short self-check to skip familiar foundations without hiding the complete roadmap.

Check only what you can already do without step-by-step help.

0/3 signals · Start from the foundations

Recommended entry: AI literacy. This does not mark lessons complete; use it to challenge out of familiar material.

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Core roadmap

Learn in phases, build as you go

Open a module when you are ready. The full lesson sequence is still preserved for resume and next-lesson navigation.

Phase 1

AI literacy

Recognize AI, its limits, and the safety decisions that come first.

Foundations and limitsBuild reliable judgment about where AI helps and where it fails.12 lessons
  1. 01AI you already use8m
  2. 02History of AI10m
  3. 03What AI can and can't do9m
  4. 04Why AI makes mistakes10m
  5. 05Bias and fairness9m
  6. 06AI Copyright10m
  7. 07Deepfakes & synthetic media9m
  8. 08AI regulation basics8m
  9. 09AI ethics scenarios8m
  10. 10Model governance basics8m
  11. 11Content safety basics8m
  12. 12Privacy and smart sharing9m
Safety and societyBuild reliable judgment about where AI helps and where it fails.1 lessons
  1. 01Your AI learning map8m
Phase 2

Practical AI use

Prompt, verify, and apply AI to useful work without outsourcing judgment.

Prompt foundationsPractice a repeatable workflow and keep a human verification step.12 lessons

Builds on: AI literacy

  1. 01Talk to AI for the first time8m
  2. 02Good prompts have a job9m
  3. 03Add context9m
  4. 04Iterate: bad to better9m
  5. 05Chain-of-Thought Prompting10m
  6. 06AI for Research10m
  7. 07Work helper9m
  8. 08AI for email9m
  9. 09AI for customer support9m
  10. 10AI for Excel10m
  11. 11AI for data analysis10m
  12. 12AI for Presentations10m
Applied workflowsPractice a repeatable workflow and keep a human verification step.12 lessons

Builds on: AI literacy

  1. 01AI for Marketing10m
  2. 02AI for legal basics9m
  3. 03No-Code AI10m
  4. 04AI for automation10m
  5. 05AI for finance basics9m
  6. 06AI for product managers9m
  7. 07AI for HR basics9m
  8. 08AI change management10m
  9. 09AI for sales10m
  10. 10AI for healthcare basics10m
  11. 11Capstone roadmap8m
  12. 12Spot wrong answers9m
Verification and habitsPractice a repeatable workflow and keep a human verification step.1 lessons

Builds on: AI literacy

  1. 01Your weekly AI habit8m
Phase 3

How AI systems work

Build a working mental model of language models, retrieval, tools, and agents.

Model foundationsConnect system behavior to the technical ideas underneath it.12 lessons

Builds on: Practical AI use

  1. 01What is an LLM?7m
  2. 02Tokens — how AI reads text6m
  3. 03Training vs inference8m
  4. 04Transformers in plain English9m
  5. 05Serving Large Language Models9m
  6. 06Temperature — safe vs creative6m
  7. 07Embeddings — meaning as numbers7m
  8. 08Vectors & similarity search7m
  9. 09RAG — look it up, then answer8m
  10. 10Inside RAG — the pipeline8m
  11. 11Hybrid search for RAG9m
  12. 12Tools — when AI takes action7m
Retrieval and toolsConnect system behavior to the technical ideas underneath it.6 lessons

Builds on: Practical AI use

  1. 01Agents — think, act, repeat8m
  2. 02Multi-Agent Systems9m
  3. 03MCP — a standard plug for AI tools8m
  4. 04Prompt injection & AI security9m
  5. 05Structured outputs & JSON mode8m
  6. 06Choosing a model9m

Optional extras

  1. 01AI Video Generation10m
  2. 02AI Voice Generation10m
  3. 03Careers in AI10m
  4. 04AI Monitoring9m
  5. 05Reasoning models8m
  6. 06Production AI Architecture10m
  7. 07Talk to an LLM from code10m
  8. 08API errors & retries9m
  9. 09LLM tracing lab10m
  10. 10Build a mini RAG11m
  11. 11Agents in code11m
  12. 12Eval gates in code10m
  13. 13Capstone: research bot with citations13m
  14. 14Deploy a RAG app11m
  15. 15Tools in code11m
  16. 16Guardrails in code10m
  17. 17Prompt injection in code10m
  18. 18Multi-agent in code11m
  19. 19Production monitoring lab10m
  20. 20Workflow automation in code10m
  21. 21AI for design10m
  22. 22AI for science10m
  23. 23Model cards and datasheets10m
  24. 24Eval for everyone10m