I’ve been building my own second brain using a knowledge graph system, and it’s completely changed how I learn AI concepts and create content. Instead of having scattered notes and random ideas floating around in my head, I now have an interconnected system that helps me see connections between different concepts and track everything I’ve learned.

So what exactly is a second brain, and why does it matter for AI engineers? Well, think about it. You’re constantly learning new technologies, exploring different frameworks, understanding new concepts. All of that valuable information needs to go somewhere. Without a structured system, most of it just disappears into the void. You watch a tutorial, read documentation, build a project, and then a month later you can barely remember the key insights you discovered.

That’s where knowledge graphs come in. Instead of linear notes that sit in isolation, knowledge graphs create connections between related ideas. When I’m exploring a concept like AI agents, my knowledge graph shows me all the related technologies I’ve worked with, all the concepts that connect to it, and all the practical applications I’ve discovered. It’s like having a map of everything in my brain, but organized in a way that actually makes sense.

How Knowledge Graphs Organize Your Learning

The key to making this work is having a clear structure. In my system, I use three levels of organization: hubs, concepts, and technologies. Hubs are the big umbrella topics that connect to many different ideas. Think of them like the major branches of AI engineering. Concepts are the recurring principles and patterns that show up across different projects. And technologies are the specific tools and frameworks you actually use.

This three-tier structure creates a hierarchy that makes sense. When I’m learning about a new AI coding tool, it doesn’t just float around in isolation. It connects to broader concepts like AI automation and workflow optimization, which then connect to even bigger hubs like AI coding assistants and development tools. Everything links together.

The really powerful part is how this helps you see patterns you’d otherwise miss. You might discover that three different projects you worked on all used similar approaches to prompt engineering. Or you might notice that certain technologies keep showing up together. These connections are incredibly valuable for deepening your understanding of AI engineering concepts and identifying what you should learn next.

AI Agents Make Knowledge Extraction Automatic

Here’s where it gets really interesting. You can use AI agents to automatically build and maintain this knowledge graph for you. Instead of manually organizing everything, AI agents can process your existing data and extract the important structured information.

Let’s say you have a collection of YouTube video transcripts, like I do. An AI agent can read through those transcripts and identify all the key concepts being discussed. It can recognize when you’re talking about Git workflows, Python frameworks, or AI agent architectures. Then it automatically creates nodes for each concept and links them together based on how they relate to each other.

The same principle works for other data sources too. If you have existing notes in Obsidian or Notion, an AI agent can ingest those and create a knowledge graph. If you have code repositories, an agent can scan through your projects and extract the interesting concepts and patterns you’ve used. You probably already have tons of valuable input data sitting around. You just need a system to organize it.

System Prompts Create Consistency

The secret to making AI agents work well for knowledge management is having really good system prompts. These prompts tell the agent exactly how to structure information, what categories to use, and how to link different concepts together.

When you have a solid system prompt, every piece of information that gets added to your knowledge graph follows the same format. Every technology node has the same structure. Every concept links to hubs in the same way. This consistency is what makes the whole system searchable and useful. Without it, you’d just have a mess of unstructured information that’s barely better than having no system at all.

Real Benefits for Your Career Growth

So what’s the actual value of all this? Well, for me, it’s been transformative for content creation. When I’m planning new videos, I can explore my knowledge graph and see what topics I’ve already covered, what angles I haven’t explored yet, and what interesting combinations of concepts might make for great new content. It helps me avoid repetition and come up with fresh ideas.

But even if you’re not creating content, a knowledge graph accelerates your learning. You can quickly review what you’ve learned about a topic, see how it connects to other areas, and identify gaps in your knowledge. It’s like having a structured learning path that adapts to what you already know.

The real power is in the connections. When you can see how different concepts relate to each other, you develop a much deeper understanding than just memorizing isolated facts. And that’s what separates good AI engineers from great ones.

To see exactly how I built my knowledge graph system and watch a live demonstration of AI agents extracting concepts from transcripts, watch the full video tutorial on YouTube. I walk through the complete process and show you how the system works in practice. If you’re interested in learning more about AI engineering and building your own second brain, join the AI Engineering community where we share insights, resources, and support for your learning journey.