AI Essentials
A broad, applied AI and LLM curriculum for everyone, from non-technical to slightly technical, foundations, prompting, daily work, responsible use, and building with AI, ending with deep dives on ChatGPT, Claude, and Gemini.
AI is not a side project anymore. It is the operating layer your competitors are already building on, and the gap between leaders who understand it and leaders who nod along in meetings is widening fast. AI Essentials closes that gap.
You start where it counts, with how AI and LLMs actually work, what they can and cannot do, and why hallucinations happen. No hand waving. Then you learn to speak to these systems properly, from prompting fundamentals to advanced patterns that get consistent, high value output instead of generic mush.
From there it gets practical. You apply AI to everyday use cases, build repeatable workflows, and put guardrails in place with real coverage of risks, verification, privacy, ethics and governance. This is the part most executives skip and later regret.
When you are ready to build, you will structure an AI project, work with tools, RAG and agents, and cover coding basics and evaluation so you know what good looks like. Then you go deep across the three ecosystems that matter. ChatGPT and the OpenAI stack, including custom GPTs, actions, connectors and the API. Claude and Anthropic, including Skills, MCP, Claude Code and the agent SDK. Gemini and Google AI, including Workspace, Gems, AI Studio and the Gemini API.
By the end you will not just have opinions about AI. You will have judgment, vocabulary, and hands on capability across every major platform your teams will actually use. That is the difference between sponsoring AI initiatives and steering them. Senior leaders who finish this track stop delegating the hard questions and start answering them.
What you'll master
- Explain how LLMs work and where they break, so you can spot hallucinations before they cost you
- Write prompts that produce reliable, executive grade output using advanced prompting patterns
- Design AI workflows that remove real hours from your week and your team's
- Set governance, privacy and verification standards that keep AI use safe and defensible
- Structure an AI project end to end, from scoping to tools, RAG, agents and evaluation
- Build and automate across ChatGPT, Claude and Gemini using their native tools and APIs
- Judge AI investments and vendor pitches with the vocabulary and hands on context to back it up
Blocks
AI & LLM foundations
What AI, machine learning, and large language models actually are, how they work at a topline level, and where their capabilities end.
Prompt engineering
The practical craft of getting great results: structuring prompts, giving examples, asking for step-by-step reasoning, and iterating fast.
AI in daily work
Turning AI into a genuine productivity multiplier for writing, analysis, research, and admin, plus the judgment to know when not to use it.
Responsible & trustworthy AI
Using AI safely and ethically: bias, verification, confidential data, copyright, and the topline of AI governance and the EU AI Act.
Building with AI
How to structure an AI project, navigate the tools landscape (RAG, agents, vector databases, APIs), write basic code, and evaluate outputs against cost and latency.
AI agents: design, build & operate
The vendor-neutral craft of agents: what an agent really is, the design patterns that actually work, how to give it tools, memory and retrieval, how to coordinate several of them, and how to evaluate, guard, and ship them to production.
ChatGPT & the OpenAI ecosystem
A deep, hands-on mastery path for ChatGPT: models and Projects, Custom GPTs, Actions and Connectors, data analysis and multimodal, Codex and Canvas, agents, the OpenAI API, automation, and a capstone.
Claude & the Anthropic ecosystem
A deep, hands-on mastery path for Claude: models and Projects, connectors, Skills, MCP, Claude Code, GitHub workflows, the Anthropic API and Agent SDK, automation, and a capstone.
Gemini & Google AI
A deep, hands-on mastery path for Gemini: models and the app, Gems, Workspace, Extensions, AI Studio and the Gemini API, Gemini CLI and Code Assist, agents, Vertex AI, automation, and a capstone.
Frequently asked questions
What does the AI Essentials curriculum cover?
AI Essentials is a nine-block, 125-lesson curriculum that runs from how LLMs actually work through prompting, daily use, responsible AI and building with AI, then goes deep on ChatGPT, Claude and Gemini. It is built for people who need working judgment about AI, not a research background.
Do I need a technical background to follow it?
No. AI Essentials starts at a non-technical level and becomes slightly technical as you progress: the foundations, prompting and daily-work blocks assume nothing, while the building block introduces RAG, agents, APIs and basic code. You choose how far up that slope you go.
Where should I start if I already use ChatGPT every day?
Start with the AI and LLM foundations block, then prompt engineering. Daily users usually know the interface but not why hallucinations happen, what a context window constrains, or which advanced prompting patterns produce consistent output, and that gap is what limits their results.
Is there a certificate or diploma at the end?
No. AI Essentials delivers no diploma and no state-recognized certification, and there is no affiliation with a school or university. Reading is free and open; an account only saves your progress through the 125 lessons.
What is the difference between the prompt engineering block and the platform deep dives?
Prompt engineering (6 lessons) teaches the vendor-neutral craft: structuring prompts, giving examples, chain-of-thought reasoning, fast iteration. The ChatGPT, Claude and Gemini deep dives (28, 26 and 28 lessons) cover what is specific to each stack, such as Custom GPTs and Actions, Skills and MCP, or Gems and AI Studio.
Do I have to complete all three platform deep dives?
No, they are independent. Each one stands alone and ends with its own capstone, so you can take only the ecosystem your organisation runs on. Leaders who evaluate vendors or arbitrate between tools tend to do all three because the comparison itself is the value.
What does the responsible AI block actually deal with?
Six lessons on bias, verification of outputs, handling confidential data, copyright, and the topline of AI governance and the EU AI Act. It is written to help you set verification and privacy standards a team can follow, not to summarise legal texts.
What is covered on AI agents beyond a general introduction?
A dedicated 10-lesson block covers what an agent really is, the design patterns that hold up in practice, giving agents tools, memory and retrieval, coordinating several agents, and evaluating, guarding and shipping them to production. It is vendor-neutral, with the platform-specific agent work handled inside the ChatGPT, Claude and Gemini blocks.