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Free course · Technology

AI From Zero to Productive

A Practical Guide to Using AI Well

A free, 35-article course on understanding and using modern AI for learning, work, coding and career growth. It teaches transferable skills, not product tours: how language models behave, how to ask and give context, how to verify answers, how to protect privacy, when to automate, and how to build your own personal AI system.

Start lesson 1 →See all 35 lessons
35
lessons
9
modules
6 h
of reading

Curriculum

9 modules · 35 lessons · about 6 h

Module 1Understanding Modern AI
  1. 01Start Here: What You Can Do with AI and the Work Loop12 min
  2. 02AI, Machine Learning, Generative AI and LLMs: The Map10 min
  3. 03How Language Models Work: Training, Inference, Tokens and Context12 min
  4. 04Why AI Sounds Right and Still Gets Things Wrong11 min
  5. 05Choosing the Right Kind of AI: Chat, Search, Notebooks and Assistants10 min
Module 2Talking to AI Effectively
  1. 06Prompt Fundamentals: Goal, Context, Constraints and Format12 min
  2. 07Give AI the Right Context: Source Material and Long Documents9 min
  3. 08Examples, Output Formats and Structured Output11 min
  4. 09Iterate, Break Tasks Apart and Reuse What Works11 min
Module 3AI for Learning
  1. 10How to Use AI to Learn a Difficult Technical Subject11 min
  2. 11From Notes to Recall: Summaries, Flashcards, Practice and Revision with AI10 min
  3. 12Learning with AI Without Cheating Yourself9 min
Module 4AI for Work and Productivity
  1. 13How to Use AI for Emails and Documents Without Losing Your Voice10 min
  2. 14Meetings, Notes and Long Documents: Summaries You Can Trust10 min
  3. 15Spreadsheets and Data with AI: Analysis You Can Check11 min
  4. 16Brainstorming, Planning and Decision Support with AI10 min
Module 5AI for Developers
  1. 17AI Coding Assistants: Modes, Strengths and Your Responsibility9 min
  2. 18Use AI to Understand Code, Codebases, APIs and Documentation10 min
  3. 19How to Use AI to Debug Code Safely9 min
  4. 20Testing, Reviewing and Refactoring AI-Generated Code10 min
  5. 21Security and Licensing Risks of AI-Generated Code11 min
  6. 22Vibe Coding vs Responsible AI-Assisted Building10 min
Module 6AI for Career Growth
  1. 23Improve Your Resume and Profile with AI, Honestly10 min
  2. 24Job Descriptions, Skill Gaps and Portfolio Projects with AI11 min
  3. 25Interview Preparation and Mock Interviews with AI10 min
Module 7Research, Verification and Responsible AI
  1. 26How to Fact-Check AI-Generated Answers12 min
  2. 27An AI-Assisted Research Workflow: Primary Sources and Citation Checking11 min
  3. 28Privacy and Security: What Not to Paste, Connected Tools and Prompt Injection14 min
  4. 29Bias, Copyright and Human Judgment12 min
Module 8Automation and AI Agents
  1. 30Chatbots, Workflows, Automation and Agents: Choose the Simplest System10 min
  2. 31Tool Use, Function Calling and Agent Loops Explained11 min
  3. 32Where Agents Fail and How to Build Reliable AI Workflows12 min
Module 9Build Your Personal AI System
  1. 33Design Your Personal AI Operating System10 min
  2. 34Workflow Recipe Cards for Study, Code, Career, Research and Work13 min
  3. 35Your 30-Day Plan and Long-Term AI Skill Roadmap13 min

This is a free course on understanding and using modern AI, written for people who are starting with little or no practical knowledge and want to become confident, careful and productive. It is not a list of the best AI tools. Tool lists go out of date within months. It teaches the skills that survive product changes: understanding what the technology is, asking clearly, giving context, checking answers, protecting privacy, improving through feedback and automating only what deserves it.

Every idea is explained in plain language first, then shown on a practical task with the goal, the first attempt, what went wrong, the improved version and how to check the result.

Who it is for

  • Students who want to learn with AI without learning less.
  • Developers who want to use coding assistants without giving up engineering judgment.
  • Fresh graduates and early-career professionals who want to work faster and more accurately, and to prepare honestly for jobs.
  • Creators and knowledge workers who write, research and plan.
  • Anyone who has used a chatbot and felt unsure whether to trust it.

The idea that ties it together: the Work Loop

Throughout the course you will use a six-step loop on every AI task that matters:

Step

Question

1. Decide

Is this a good job for AI, and how much checking will it need?

2. Describe

Have I stated the goal, audience, constraints and format?

3. Ground

Have I given it the material to work from, or asked for sources?

4. Check

What must I verify before I rely on this?

5. Refine

What is missing or wrong, and how do I tell it?

6. Systemise

Will I do this again? Can it become a routine or a workflow?

Understanding how the technology works (Module 1) sits underneath all six.

How the course is organised

Start with the first article and go in order, because each one builds on the ones before it. There are 35 articles in nine modules.

Module

Articles

What it builds

1. Understanding Modern AI

Start Here and the Work Loop, The Map: AI, ML, Generative AI and LLMs, How Language Models Work, Why AI Gets Things Wrong, Choosing the Right Kind of AI

A correct mental model, and how to choose tools by capability

2. Talking to AI Effectively

Prompt Fundamentals, Context and Source Material, Examples, Formats and Structured Output, Iterate, Decompose and Reuse

Prompting as task design, with grounding, examples and templates

3. AI for Learning

Learn a Difficult Subject, Notes to Recall, Integrity and Dependency

Tutoring, recall and practice, and honest, independent learning

4. AI for Work and Productivity

Emails and Documents, Meetings, Notes and Long Documents, Spreadsheets and Data, Brainstorming, Planning and Decisions

Faster, more accurate everyday work

5. AI for Developers

Coding Assistants and Responsibility, Understand Code and APIs, Debug Safely, Tests, Review and Refactoring, Security and Licensing, Vibe Coding vs Responsible Building

Using assistants without surrendering judgment

6. AI for Career Growth

Resume and Profile, Honestly, Job Descriptions, Skill Gaps and Portfolio, Interview Preparation

A truthful, evidence-based job search

7. Research, Verification and Responsible AI

Fact-Check AI Answers, Research Workflow and Citations, Privacy, Security and Prompt Injection, Bias, Copyright and Human Judgment

Verification, safe data handling and accountability

8. Automation and AI Agents

Chatbots, Workflows, Automation and Agents, Tool Use and Agent Loops, Reliable Workflows and Human-in-the-Loop

Understanding agents without hype, and building dependable systems

9. Your Personal AI System

Design Your Personal AI Operating System, Workflow Recipe Cards, 30-Day Plan and Long-Term Roadmap

Your own rules, playbook, schedule and roadmap

About the code

A few articles include small Python programs. Complete programs start with a comment such as # File: validate_output.py that tells you what to name the file, and are followed by the output that they printed when the course was written. Programs that stand in for AI models are toys with fixed results, so the output is the same each time. Fragments without the file comment are illustrations. You need Python 3.11 or later to run them, and you do not need to run any of them to follow the course.

How to study

  1. Go in order for Modules 1, 2 and 7, which are core for everyone.
  2. Choose a track for the middle modules: students read Module 3, working professionals Module 4, developers Module 5, job seekers Module 6.
  3. Try every example on your own tasks. The point is transfer.
  4. Attempt each exercise before opening its solution.
  5. Save one reusable item per article in a notes file: a prompt, a rule, a checklist.
  6. Use the 30-day plan in the last article to turn reading into practice.

How to verify AI output

Every module returns to the same habit: match your checking to the stakes. For anything that matters, break the answer into claims, find primary sources, compare independent ones, check dates, context and numbers, and record what you found. How to Fact-Check AI-Generated Answers gives the full routine, and the earlier articles show where errors hide.

How to avoid depending on AI

AI is most valuable when it makes you do more of the thinking that matters. The learning articles teach attempt-first habits, hint ladders, cold tests and a personal AI-use agreement. If you notice that you cannot start without the assistant, or that you can follow an answer but not produce one, treat that as feedback and adjust.

How the course handles fast-changing products

AI products change quickly. This course handles that in four ways:

  • Concepts first. Lessons teach ideas that last: context, grounding, verification, privacy, evaluation and human judgment.
  • Products as examples. Where a lesson names a product, it is an example of a category of tool, and the lesson is written so that it still works if the product changes.
  • Dates where it matters. Statements about product features, privacy terms or laws are marked "as of October 2026" where they could change.
  • Check the source. For any capability, price, privacy term or legal point, check the product's current official documentation or the official legal text before relying on it.

Must-know and nice-to-know

Not every topic carries equal weight. As a rough guide:

  • Must-know for everyone: how language models work and why they err (articles 3 and 4), prompt fundamentals and grounding (6 and 7), fact-checking (26), privacy and prompt injection (28), and the personal system (33).
  • Core for your track: the module for students, professionals, developers or job seekers.
  • Nice-to-know depth: structured output and templates (8 and 9), research workflow (27), bias and copyright (29), and the automation modules (30 to 32) unless you build or buy workflows.
  • Optional: the recipe cards (34) and long-term roadmap (35) are most useful once you have practised for a few weeks.

Honest limitations

  • AI tools evolve, and some details here will age. That is why the course teaches habits before features.
  • The course gives general information. It is not legal, medical, financial or security-certification advice.
  • Examples and people named in them are illustrative.
  • Product details were checked against official documentation where it was available at the time of writing. A few legal points came from secondary sources and are flagged for re-checking. Everything may have changed since.
  • Nothing here promises a particular result. The best evidence of whether it helps you is your own before-and-after measurement, which the 30-day plan shows how to take.

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