Set a learning goal you can check: a task you can do and explain. AI can supply examples, question your reasoning, and find gaps in an attempt. You still need to make the decisions that show you understand the work.
Austin Marchese’s self-education video organizes that work around six questions. The guide below adapts his sequence into a practice routine, with source checks and an unaided attempt added as tests. It makes no promise about how quickly you will become an expert.
01Choose the task and the depth
Curiosity is enough reason to learn something. For a work skill, name the task that learning should make possible. Marchese starts with the problem holding up his business; Automate the step that slows you down covers finding that problem before choosing a tool.
Use these questions to keep the study tied to the task:
| Question | Write down |
|---|---|
| Why learn this? | The problem you need to solve |
| How much must I understand? | A task you can perform and explain |
| Who has done this before? | A source with relevant experience and evidence |
| How will I practice? | A small attempt with a checkable result |
| How does it fit here? | The team’s current process and constraints |
| What should I learn next? | The specific gap the attempt exposed |
For example, replace a goal of learning spreadsheets with producing a weekly sales total from an export, checking the total, and explaining how duplicate rows affect it. That gives you a stopping condition and a way to choose useful lessons.
02Give the tutor useful sources
Supply your goal, what you already understand, and an example of the work. Ask for a plain explanation, then one applied to your task. The briefing method in Brief the model like a brilliant new hire applies to tutoring too. Include the needed context in the session rather than assuming the tool already knows your background.
Choose sources you can inspect: current documentation and a practitioner’s worked example. Ask where the example’s conditions differ from yours. Adding documents gives the model reference material; it does not train a new expert model.
Marchese uses NotebookLM for this source collection. Google’s current help page, titled Use chat in Gemini Notebook, documents source selection and citations that open the cited passage. It also describes optional capabilities that search the web. Ask for answers from your selected sources and inspect the citations before adopting a recommendation.
Open the passage supporting a key claim and check both its meaning and its fit to your task. For a larger collection, Build a knowledge base your team can ask covers retrieving relevant material.
03Attempt, explain, check
Make one small attempt before asking AI to complete the task. Then explain your decisions without reading its answer. Marchese suggests speaking the explanation aloud and having Claude ask follow-up questions. Typed explanations work for this exercise too.
“The goal here is to identify any gaps in your knowledge.”
Austin Marchese, “The Art of Becoming Self-Educated with Claude” · 12:39Anthropic’s Learning mode announcement describes asking learners to explain their approach and support conclusions with evidence. The following original prompt requests that questioning style in an ordinary chat; it does not enable a product mode.
My task: [what I need to do]
Success check: [how I will check the result]
Sources: [documents or excerpts]
My attempt and explanation: [paste both]
Ask one question about a decision in my attempt, then wait for my answer. Let me try before giving a correction. Tie factual corrections to a supplied source and flag anything the sources cannot settle. After we address the gaps, give me a changed example to attempt unaided.
Check the result against the task’s success condition as well as the tutor’s feedback. In the spreadsheet example, calculate a small sample’s total both with your formula and by hand, then compare them. The model’s praise does not verify the total. For practice answering another person’s questions aloud, see Rehearse the interview with an AI coach.
04Check how the work happens now
Before applying the lesson to a shared process, write down its current steps and ask the person who does the work to correct them. Marchese recommends a flowchart. Mark any step you have assumed so the chart cannot quietly turn a guess into a fact.
Compare the proposed change with that checked process. Name who depends on the result and what must stay compatible. If the problem itself is unclear, Take a problem apart before AI solves it covers separating facts from assumptions.