Have you ever watched someone speak and know, without an ounce of doubt, that they have mastered their subject?

Someone who jumps out to me who exudes expertise is Nate B Jones on TikTok.

What immediately strikes me about Nate wasn't just his knowledge of AI—it was his absolute fluency in explaining complex concepts. He doesn't stumble or resort to vague generalisations. Instead, he speaks with clarity and confidence, breaking down complex topics into digestible explanations.

That is the kind of mastery we're all aiming for.

And we’re going to apply a final step to reach that level of fluency. We’ll leverage the Feynman technique - teaching is one of the most powerful learning tools available to us.

When you have to explain something to someone else, you cannot hide behind vague understanding or fuzzy concepts. You must clarify your own thinking, organise your knowledge, and identify gaps in your understanding. Otherwise you cannot explain to others.

Let's get started:

The Feynman Technique

Named after Nobel Prize-winning physicist Richard Feynman, this technique is based on a simple premise: If you can't explain something in simple terms, you don't really understand it.

The traditional Feynman Technique involves four steps:

1. Choose a concept

2. Explain it to a 12-year-old

3. Identify gaps and go back to the source material

4. Simplify and use analogies

Your AI tutor makes an ideal partner for practicing this technique. Unlike a real person who might nod politely even when your explanation is confusing, your AI tutor can provide specific feedback on unclear elements and prompt you to refine your understanding.

Implementing the Feynman Technique with Your AI Tutor

Here's how to use your AI tutor to apply the Feynman Technique:

1. Select a key concept from your recent learning sessions. Choose something substantial enough to be challenging but defined enough to explain in a few minutes.

2. Ask your AI tutor to play the role of someone unfamiliar with the topic. For example: "I'd like to practice explaining machine learning algorithms. Could you pretend to be someone with no technical background, and I'll try to explain how they work in simple terms? Listen until I tell you I am done. Then provide feedback on the clarity of my explanation"

3. Deliver your explanation without referencing your notes or previous conversations in your project. Preferably use voice mode to deliver verbally.

4. Request targeted feedback on your explanation: "How clear was my explanation? Were there parts that seemed confusing or overly complex? Did I rely too much on jargon?"

5. Identify gaps in your understanding based on the feedback.

6. Refine and retry until you can explain the concept clearly and confidently.

This process forces you to transform passive knowledge into active understanding. The AI's feedback helps identify exactly where your explanation falls short, allowing you to focus your review efforts precisely where needed.

Multi-Perspective Teaching

For bonus points and to deepen your understanding, practice explaining the same concept to different imagined audiences:

• To a complete beginner: Forces you to avoid jargon and focus on fundamentals

• To a peer with basic knowledge: Allows more technical language but requires clarity

• To an expert in an adjacent field: Challenges you to make relevant connections

• To a skeptic: Prepares you to address common misconceptions and objections

Adapt this depending on who you think you’ll end up talking to about these concepts. Your AI tutor can role-play these different audiences, asking appropriate questions from each perspective.

Teaching-to-Camera Method: Building Confidence and Clarity

Taking your teaching practice to the next level involves recording yourself explaining concepts—a technique used by many top educators like Nate B Jones.

Recording yourself creates several powerful benefits:

• Increases accountability (you can't gloss over concepts you don't fully understand, it’s right there on camera!)

• Builds presentation confidence

• Creates a record of your learning journey

• Develops communication skills valuable in professional contexts

• Forces additional clarity and conciseness

Implementing the Teaching-to-Camera Method

Here's a simple process to implement this approach:

1. Concept selection: Choose a single concept you've learned recently.

2. Outline preparation: Work with your AI tutor to create a simple outline covering:
   • What the concept is (definition)
   • Why it matters (relevance)
   • How it works (explanation)
   • When/where it applies (application)

3. Recording: Use your phone or computer to record a 2-3 minute explanation of the concept without referring to notes.

4. Publish: we need this to be real. Publish your work to TikTok, Instagram, Youtube Shorts and/or LinkedIn.

You don't need to share these recordings publicly (though this will certainly sharpen your practice!). The act of recording itself dramatically improves clarity of thought and expression.

If you do want to share your learnings publicly (in particular if you’re learning about AI) check out the AI Authority Accelerator.

Learning Through Doing

One final piece! Action.

The ultimate test of learning is not what you know but what you can do with what you know. While teaching forces conceptual clarity, application projects ensure practical understanding. We introduced these briefly in Part 4, but let's explore how to design them specifically to cement learning.

We do this through focused mini projects.

Your AI tutor can help design custom projects tailored to your learning goals. Here's a template prompt:

I'd like to create a time-boxed project to apply what I've learned about [concept].
Could you design a 30-minute exercise that:

1. Focuses specifically on applying [specific aspect or technique]
2. Produces a tangible result I can evaluate
3. Relates to my goal of [your broader learning objective]
4. Can be completed with the tools and resources I have available
5. Includes clear evaluation criteria so I know if I've understood correctly

Please make this practical rather than theoretical, and ensure it's genuinely completable within the time limit.

For example if you are learning Python this prompt might gives projects like:

• Build a specific function to solve a defined problem (30 min)

• Debug a provided code sample with intentional errors (20 min)

• Implement a particular algorithm from scratch (45 min)

Use these sort of projects (customised to what you’ve been focusing on!) to completely cement your learning. As a bonus : maintain a collection of your completed mini-projects, teaching videos, and real-world applications. This creates a tangible record of your growing expertise. Very neat.

Building Your Sustainable Learning System

We've covered a lot of ground in this series! Let's bring it all together into a sustainable system you can maintain long-term.

The Complete AI-Assisted Learning Cycle

1. Setup: Configure your AI tutor with clear goals and methodologies (Part 2)

2. Acquisition: Conduct structured learning sessions with active engagement (Part 3)

3. Reinforcement: Implement spaced repetition and active recall (Part 4)

4. Cementation: Teach concepts and apply knowledge through projects (Part 5)

Here's what a sustainable weekly schedule might look like:

This represents about 2.5 hours per week—a modest time investment that produces substantial results when approached systematically. And because it’s all highly tailored to you it’s like having a 1:1 tutor on call.