From Notes to External Brain: Preparing My Memory for AI
How I’m restructuring 1,000+ messy notes into a knowledge system both humans and AI can understand.
Why I’m Doing This
In my previous articles, I looked at two big ideas:
Why context is critical for getting high-quality outputs from AI
Why AI needs memory to enrich that context
Those ideas set the stage for the experiment I’m starting now: externalising my own memory so it can eventually be used by AI when solving tasks.
Starting With My Own Notes
I want to build what’s often called a Second Brain — an external memory I can rely on. For me, that means taking my messy collection of personal notes and slowly transforming them into a structured, AI-readable knowledge base.
Right now, I’ve got more than 1,000 notes sitting in Apple Notes.
It’s not a system — it’s a data lake. A place where thoughts, quotes, market insights, and half-finished ideas live side by side, waiting to be organised.
And this “lake” is where my journey begins.
The Roadmap
When I thought about turning my personal notes into something closer to an external memory, I knew I needed a roadmap.
For me, it comes down to three big steps:
Define a structure for my notes (build a knowledge model).
Create rules for how information flows in and out (protocols for adding new knowledge entries and retrieving old ones).
Build connections between notes (so it becomes more than just a library — something I can use for real sensemaking and solving complex tasks).
Following this roadmap will eventually allow me to convert my notes into a machine-readable format (e.g. choosing the right database and retrieval method so AI can start working with them the same way I do).
That’s the first real step toward self-automation.
So let’s begin where it all starts: by analysing my own messy collection of information — the raw material I want to turn into what many people call PKM, or Personal Knowledge Management.
The Current State of My Notes
Right now, my “knowledge base” is nothing more than 1,000+ scattered notes which I’ve collected over past 2 years. No structure. No system.
Which means that when I actually need to use them (whether for work projects, writing, or even planning a trip) I can rarely find what I’m looking for.
That was a sobering moment.
I caught myself asking: “If I can’t retrieve what I need, what’s the point of taking notes at all?”
That’s when I realised: to truly navigate my externalised mind palace, I need a map. A structure that lets me move quickly between rooms, find the right shelf, and retrieve the right piece of knowledge.
And defining that structure? That’s the very first step on this journey.
Step 1: Building a Knowledge Model — the Backbone of My External Brain
If I want to transform my scattered notes into a structured knowledge base, I need more than folders and tags. I need a knowledge engineering framework which is a model that gives shape to chaos.
For this, I turned to the idea of ontologies.
Think of an ontology as a mental framework, a schema that helps you organise and interpret information. (The roots of this go back to Bartlett’s Schema Theory in 1932.)
At its core, a schema is a logical blueprint. It defines the features of knowledge: concepts, hierarchies, properties, and the relationships between them.
Here’s how I’m adapting that blueprint for my own notes:
Concepts: mind spaces, tasks, quotes, ideas, reflections
Containers & hierarchies: folders → subfolders → notes
Labels: tags that act as metadata for analysis and retrieval
This structure will be the backbone of my external brain — a way to classify, connect, and navigate my notes across different topics and projects.
Step 2: Create Rules for How Information Flows In and Out — Ensuring Consistency in PKM
A structure by itself isn’t enough.
If I organise my existing notes but then go back to adding new ones in the same chaotic way I did before, the structure won’t survive.
It’ll collapse under the weight of inconsistency. (That’s exactly what happened with my early PKM experiments I wrote about earlier.)
So what was missing?
A schema.
A schema acts like a map of my mind palace:
When I add a new piece of knowledge (a data entry), the schema helps me get into the “right headspace,” find the right container of knowledge, and annotate the information with a standard set of metadata.
When I need to find and extract existing knowledge, I use the same schema for navigation.
Think of it as a grid map that helps you take shortcuts to the exact information you need to retrieve.
And here’s the surprising part: the schema has even more to offer.
Step 3: Creating Relationships Between Notes — the Key to Making Them Usable
Beyond serving as a map for navigating my mind palace, the schema also works as a guide for two critical things:
A playbook for classification: defining the type and purpose of each note, assigning it to the right container (folder), and tagging it with the right metadata.
A rulebook for linking notes: creating interconnections that make the knowledge base dynamic. For example, a note about the Thousand Brains Theory might live in my “AI” research folder (within the “Areas of Interest” mind space), but also link to “Projects” when I use it to design AI solutions, and “Beliefs” when I reference it in a blog post about AI.
If the schema defines the knowledge spaces (containers) and their locations in my mind palace, then the relationships are the network of roads between those spaces.
Thanks to those “roads”, I don’t have to enter every room one by one when retrieving knowledge — I can move faster using shortcuts provided by the network of connections.
This is also how learning through movement happens: as I move from one room in my mind palace to another, I start to see how the entire palace connects, and how it reflects a model of the world itself.
That’s where sensemaking takes place. And this is what makes a knowledge base more than just storage.
It’s the relationships that allow me to pull the right piece of knowledge, at the right time, to solve the task in front of me.
Formalising My Personal Knowledge Retrieval System
Our brains are incredible, but they weren’t built to hold and manage vast amounts of information.
I feel that limitation every time I tackle a task that requires pulling together multiple pieces of knowledge at once.
That’s why my bet is on externalising memory — building a structure that allows me to:
Know where to find information — e.g., which folders or subfolders contain the relevant note.
Design knowledge retrieval pipelines — e.g., for preparing a presentation on AI, I might pull in:
Insights I’ve recently read about AI
Storytelling techniques for shaping those insights into a compelling narrative
Visualisation methods for presenting them effectively
Move knowledge into working memory — extracting what I need from “long-term memory” (aka my Second Brain) and loading it into my “working memory“ (i.e. “short-term workspace”), where I can start applying it directly to the task.
Before I even think about automating this with AI, I need to make sure my manual knowledge retrieval workflow works reliably.
And that’s exactly what I’ll explore in the next article — putting this system to the test with the real tasks I face every day.
Amazing!