Since ChatGPT launched, intelligence has scaled roughly 60,000x, while memory has scaled only 100x.
This means that relative to intelligence, the memory problem has gotten ~2.5 orders of magnitude (or 25x) WORSE since ChatGPT launched. Not better. 25x worse.
This explains why we have invented entire jobs called Context Engineering.
Why there is a hundred billion dollar industry for memory solution vendors.
Why so much of prompting is about what you can put into a chat to remember.
Most of all, why everyone I talk to is trying to figure out desperately how to fit transcripts, notes, documents, into some kind of order so they can use AI with it.
It’s so hard. And we’re not getting help, and that’s why I wrote this piece.
I’m gonna dig in here and explain:
Why memory is such a difficult problem to solve
Including 5 root causes that explain why no vendor has solved memory yet
I have not seen anyone lay these out this clearly before
Frankly, I think a lot of vendors don’t want to talk about them
The key insights you need to understand how to build your own memory solutions
No coding is not needed!
Yes, I have prompts to help
The 8 scalable principles I use to construct memory systems
Yes, they scale from ChatGPT user level to engineering level
Yes, you can use the same principles to design agentic systems
No coding is needed—but you CAN use these principles to code quickly
Again, I haven’t seen anyone lay these out before like this
Five Prompts to get you started on your own memory solution
An easy start prompt to help you sketch out a system
A Memory Architecture Designer prompt
A Context Library Builder prompt
A Project Brief Compiler prompt
A Retrieval Strategy Planner prompt
I wrote these prompts because I wanted to be helpful and I could find ZERO prompts out there to help with designing real memory systems in depth. I think that’s because the problem is so difficult.
So I built them myself, and I’m sharing them here. Dig in, enjoy, and good luck building a memory system for AI!
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