The new skill in AI is not prompting, it's context engineering

Can you link to the research on millions of different terms and stable long contexts? I haven't come across that yet.

I've been finding a ton of success lately with speech to text as the user prompt, and then using https://continue.dev in VSCode, or Aider, to supply context from files from my projects and having those tools run the inference.

I'm trying to figure out how to build a "Context Management System" (as compared to a Content Management System) for all of my prompts. I completely agree with the premise of this article, if you aren't managing your context, you are losing all of the context you create every time you create a new conversation. I want to collect all of the reusable blocks from every conversation I have, as well as from my research and reading around the internet. Something like a mashup of Obsidian with some custom Python scripts.

The ideal inner loop I'm envisioning is to create a "Project" document that uses Jinja templating to allow transclusion of a bunch of other context objects like code files, documentation, articles, and then also my own other prompt fragments, and then to compose them in a master document that I can "compile" into a "superprompt" that has the precise context that I want for every prompt.

Since with the chat interfaces they are always already just sending the entire previous conversation message history anyway, I don't even really want to use a chat style interface as much as just "one shotting" the next step in development.

It's almost a turn based game: I'll fiddle with the code and the prompts, and then run "end turn" and now it is the llm's turn. On the llm's turn, it compiles the prompt and runs inference and outputs the changes. With Aider it can actually apply those changes itself. I'll then review the code using diffs and make changes and then that's a full turn of the game of AI-assisted code.

I love that I can just brain dump into speech to text, and llms don't really care that much about grammar and syntax. I can curate fragments of documentation and specifications for features, and then just kind of rant and rave about what I want for a while, and then paste that into the chat and with my current LLM of choice being Claude, it seems to work really quite well.

My Django work feels like it's been supercharged with just this workflow, and my context management engine isn't even really that polished.

If you aren't getting high quality output from llms, definitely consider how you are supplying context.

Which I think it is double funny because, given the zeal with which companies are jumping into this bandwagon, AI will bankrupt most businesses in record time! Just imagine the typical company firing most workers and paying a fortune to run on top of a schizophrenic AI system that gets things wrong half of the time...

I really dislike the new ChatGPT memory feature (the one that pulls details out of a summarized version of all of your previous chats, as opposed to older memory feature that records short notes to itself) for exactly this reason: it makes it even harder for me to control the context when I'm using ChatGPT.

If I'm debugging something with ChatGPT and I hit an error loop, my fix is to start a new conversation.

Now I can't be sure ChatGPT won't include notes from that previous conversation's context that I was trying to get rid of!

Thankfully you can turn the new memory thing off, but it's on by default.

I wrote more about that here: https://simonwillison.net/2025/May/21/chatgpt-new-memory/

I am not a fan of this banal trend of superficially comparing aspects of machine learning to humans. It doesn't provide any insight and is hardly ever accurate.