What is Context Engineering in AI and Why You Need PKM to Succeed at It
How to structure your personal knowledge for effective context retrieval when prompting AI.
Lack of Context = Low-Quality AI Output
AI has extraordinary creative power.
But to fully harness it, you need more than good tools - you need good prompts. And good prompts start with context.
High-quality prompting isn’t just about syntax. It’s about knowing what you want to achieve (your vision of success) and how you want it to look (your taste). In other words: you need to engineer the context for the AI to work with.
But here’s the problem: most of this context lives only inside your head. And unless you’ve structured it in a way AI can access and understand, you’ll keep getting generic, low-value outputs.
That’s what this article is about: understanding how to externalize and structure context so that AI can generate results you actually want.
Sufficient Context = High-Quality Output
You’ve heard the rule: garbage in, garbage out.
It applies more than ever in the age of generative AI. Without context, your prompts are just noise. And the output will be, too.
To give AI sufficient context, you first need to understand your own goals and intentions. And that’s not as easy as it sounds.
Try it yourself: the next time you’re drafting an email or building a slide deck with AI, ask yourself:
What am I trying to achieve?
What does “good” look like to me?
What information would I need to make this decision?
You’ll quickly realize how hard it is to translate that internal clarity into an external prompt. That’s the essence of context engineering.
Context Engineering Takes Time
I’ve been through this too.
Before submitting a prompt, I’d spend 15 to 20 minutes just trying to figure out what I actually wanted and how to express it.
Eventually, I realized I couldn’t keep doing this from scratch every time. I needed to design myself first: to make my goals, preferences, and thought patterns visible and accessible not just to me, but to my AI tools.
Otherwise, every prompt would start with an existential crisis.
Searching for Ways to Reduce Context Engineering Time
In early 2023, I began building my first “context library.”
I started collecting references: snippets from books, social media posts, client feedback. And dumping them into Apple Notes.
By summer 2024, I had over 1,000 notes. But I was barely using them.
That’s when it hit me: collecting is not the same as retrieving. I needed a system to help me find, update, and apply these fragments of context when I needed them.
That’s when I turned to Personal Knowledge Management (PKM).
How PKM Saves Time When Prompting with AI
If context engineering is the new bottleneck, PKM is the new unlock.
I tested several PKM systems:
ACE by Nick Milo (a structure for mapping ideas)
Zettelkasten (a classic method for organizing connected thoughts)
PARA by Tiago Forte (Projects, Areas, Resources, Archives)
My early system included “evergreen” index notes (basically personal knowledge hubs) and content notes with actual highlights, references, or insights.
By the summer of 2024, I had a working PKM setup inside Apple Notes. I had saved over 500 new notes, mostly from reading, conversations, and project debriefs.
A year later, something changed:
~30% of my notes had been reused and updated.
I was using them regularly in prompts and workflows.
My speed and output quality improved significantly.
But even then, something was missing.
Designing a PKM System for AI Context Retrieval
Most PKM systems were built in the pre-AI era. They’re optimized for human reading and writing - but not for machine assistance.
They don’t include any structure for your identity: your taste, tone, decision-making patterns, or strategic goals. Why would they? In the past, you didn’t need to teach a machine who you were.
But now you do.
That’s why I’m building a new kind of PKM: a system for designing a digital version of yourself.
This isn’t just note-taking. It’s identity modelling.
I want AI to understand me the way a creative partner would.
To get there, I’m building a system that captures:
My preferences
My knowledge map
My style and voice
My strategic goals
In the next article, I’ll show you how I’m doing it.
Stay tuned.