::lesson library · 45 lessons · 5 levels · free · cc-by 4.0
The full library.
Every lesson, grouped by level. Pick any one. They're self-contained — designed to be useful even if you skip the path order.
Novice
Day zero. Has not typed into an AI chat in any serious way.
I'm scared of AI · the calm starting point
Before any lesson, the feeling. Whether you are scared, skeptical, exhausted by the hype, or quietly excited and hiding it — this is the door. None of the feelings are wrong. The path is yours.
What AI actually does — autocomplete at huge scale
Strip the magic feeling off. Get the working model of what AI is doing under the hood, so the rest of the curriculum has a foundation.
Your first real prompt — be specific, not polite
Stop typing into AI like you're texting a friend. The prompt is the entire skill at this level.
When AI gets it wrong — see a hallucination, on purpose
You will not respect the verify rule until you watch AI lie to your face with full confidence. Do it now, on a low-stakes question, where the cost is zero.
System prompts — telling AI who to be
Every AI conversation has a hidden first instruction. Knowing how to set yours is the difference between a generic answer and one calibrated to you.
Learner
Has used AI 6–30 times. Sees the shape of the conversation.
Refine, don't restart — the second draft is where it lands
The biggest skill jump at this level: stop deleting the conversation and starting over when an answer is wrong. Refine in-place.
The verify rule — three categories of trust
Not everything AI says needs verification. Most things don't. Knowing which third does is the skill.
Your saved-prompt library — the second-biggest leverage
The first time you write a good prompt for a recurring task, save it. The second time, you reuse it. By month two, your prompt library is doing 60% of the work.
Refusal posture — knowing what your AI won't say
Every AI refuses different things in different ways. Map the refusal shape of the tool you actually use, instead of guessing or repeating internet rumors.
Few-shot — teach by example
Three good examples will outperform a one-paragraph instruction every time. The skill is curating the examples.
User
AI is part of your weekly rhythm. You have prompts you reuse and a working sense of when AI helps and when it doesn't.
Multi-turn conversations — letting the chat build a model of the task
At User level, a single prompt is rarely the win. A 5–10 turn conversation that builds a working model of your task is.
Documents in chat — when paste vs. upload matters
AI is at its best when reading something specific. Knowing how to feed it documents is the next leverage step.
Your first paid tier — which one, when, why
Free tier is enough for most humans for 30+ days. When you outgrow it, you pay for ONE tool. Not four.
Image-in-chat — paste the screenshot
Most people describe what they see when they could just paste the screenshot. The AI reads pixels better than you can describe them. Stop typing the picture.
Voice mode — when speaking beats typing
Real-time conversation with AI is a different shape than chat. Knowing when to switch modes is the actual skill.
Projects and Custom GPTs — stop re-explaining yourself
Every chat starts cold. A Project remembers your background, your style, your files. Create one for the work you actually do every week, and stop pasting the same context twelve times a day.
Artifacts and Canvas — the side panel that runs your work
Claude Artifacts and ChatGPT Canvas turned chat into a workspace. Code runs. Documents render. Edits happen in place. This is where AI stops being chat and starts being a tool.
Operator
You run real work through AI daily. Multiple tools, multiple models, saved prompt library, honest mental model of the limits.
Local AI · Ollama — privacy, offline, and the limit of free
At Operator level you need an honest opinion about local-only AI. Even if you don't use it daily, you should have run it once.
Model routing — switching between Claude, GPT, Gemini mid-task
Operators don't pick one AI. They route each task to the model that does it best. Knowing the strengths is the skill.
MCP servers — the plug socket that turned AI into a real tool
Model Context Protocol is the standard plug. Knowing what plugs in changes what your AI can actually touch — your files, your inbox, your calendar, your repos.
Agent mode — when AI takes action, not just answers
The frontier of useful AI is agents that DO things — browse, click, file, send. The actual skill is the safety pattern, not the magic.
Computer use — when AI takes the mouse and keyboard
Claude in Chrome, ChatGPT Atlas, computer-use beta — the frontier is AI that drives your browser like a human. Knowing the safety pattern is the actual skill.
What AI cannot replace — taste, judgment, relationships
The operators winning in 2026 are the ones who learned what AI is for and what is theirs. Knowing the line is more valuable than any prompt.
Agents 101: model plus tools plus loop
An agent is a model with tools running in a loop until done · know when you need one and when you don't.
MCP: structured tools for AI
Model Context Protocol is the USB-C of AI tooling · learn the shape before you wire anything.
Skill primers: teach a session your context in 30 seconds
A skill is a reusable file that primes a fresh AI session with your project, voice, and rules · stop re-explaining yourself.
Local models with Ollama
Run Llama, Qwen, or Mistral on your own laptop · no API, no logs, no monthly bill for the work that should stay home.
Vision models: when to use them
Vision lets the model see images · powerful for screenshots and diagrams · weak for precise spatial work · know the line.
Audio and Whisper transcription
Whisper turns audio into text · meetings, voice memos, interviews · the AI-era replacement for note-taking.
RAG vs long context: when to retrieve, when to dump
RAG fetches the right slice of your data at query time · long context stuffs everything in · know which problem you actually have.
Embeddings: meaning as numbers
An embedding is a list of numbers that captures the meaning of text · learn the shape and you unlock semantic search, deduplication, and clustering.
Fine-tuning vs prompt engineering
For individuals, fine-tuning is almost never worth it · know exactly when it actually is.
AI safety in personal use
PII, NDAs, financial data, and other people's secrets · know the rules of what you do not paste.
Multimodal prompting: combining text, image, audio
The strongest prompts use the medium that fits the question · sometimes you describe, sometimes you show, sometimes you do both.
Chain-of-thought: making the model show its work
Asking the model to reason step-by-step before answering raises accuracy on hard problems · know when it earns its cost.
Tool use and structured output
Function calling makes the model return JSON your code can use · know the contract before you build on it.
Cost optimization: tokens, caching, model selection
AI is metered · the operators who stay profitable measure what they spend and choose the model that fits the task.
Pilot
Runs multiple projects through AI from one cockpit. Mission graphs. Receipts. Multi-model routing. The chat box is a tool inside a system, not the system itself.
Outgrowing the chat box — when chat isn't the right surface anymore
At Pilot level the chat box is a tool, not the system. You need persistent project memory, multi-tool routing, and receipts on disk. This is the bridge to a cockpit.
Receipts and paper trail — audit your own AI use
At Pilot level, what AI did for you last month becomes evidence. Knowing how to keep that evidence is the skill.
AI for kids and teachers — the next-generation curriculum
If you are a parent, teacher, or tutor — the children in your life are going to use AI for school. The choice is whether they learn it with you, or alone in their room at 11pm the night before the essay is due.
The senior-engineer pattern — talk to AI like a senior
A junior asks for the answer. A senior asks for tradeoffs, edge cases, alternatives, and reasons not to do the thing. Run that same five-step pattern through any AI conversation and the output roughly doubles in quality.
Long-context strategy: when 200K is right, when chunking wins
Long context is a tool, not a default · know what degrades, what costs you, and when chunking beats stuffing.
Open weights vs closed weights
When the model file is on your machine, the rules change · know what you gain, what you give up, and what stays the same.
AI receipts: building your own audit trail
If you cannot replay what the AI did and why, you cannot debug it, defend it, or trust it · build receipts now, thank yourself later.
Voice cloning: ethics and practical workflows
Cloning your own voice unlocks real workflows · cloning someone else's is a consent question with legal teeth · know the line.
::part of /learn · the AtomEons AI literacy curriculum