From one piece Context engineering with Dex Horthy 12 beliefs, in the piece's order there
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Their words
We can we can teach we can teach somebody, I think, to be a really good AI developer in a few months. You can build enough intuition where you are, you know, accelerated off the ground and you can go like keep growing there. It's really hard to teach someone a CS undergrad program in in 3 months.
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Their words
there's four things in your context window that matter. There's like the size of it, how many tokens? There's like the quality of the information is like is there any incorrect information? Like if the model had some thinking trace where it decided the wrong thing was true. Is there missing information? Does this like have context missing that it should have? And then there's the trajectory.
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But if you don't have good LLM intuition, like 100K for smaller models, 200K for these like really beefy like Codeex and Opus 4.8 models is usually a good like training wheel guideline of like if you pass there, your quality of results may be degrading.
+ 9 more
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How do we control that in such a way that we get the best results possible which means doing as much work as possible in the smart zone the you know first 100,000 tokens of the context window.
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you can slow way down and read every PR and read every line of code. Uh, and then you're only going to really get modest benefits from AI because that becomes I I think you should expect maybe 30 to 50% lift in productivity is kind of what I see when we go into teams
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if you want to do loops engineering, you should build one loop at a time and you should keep them small and contained. Basically, I think everything except stop reading the code is really good advice.
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yes it will catch things and it will raise your floor but I don't believe like the model writing the code is the same model reading the code and if you ask a model hey is this code good it's going to be like oh yeah it's great comprehensive it's got unit tests
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Their words
But it's basically this idea that like the only thing that made claude code good was reinforcement learning. And the dimension along which it got good was like we made a model. We trained the model and the harness together. And so the model got really good at calling the specific tools in that harness.
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the problem with training models on maintainability is like the cost function of bad architecture and bad program design can't be evaluated by running the unit test because it hits you 3 to 6 months later
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You're not actually getting a like smarter model. like the intelligence of the model is is what drives its ability to attend to all of the tokens in the context window to figure out on the next turn which parts of this 100k or 200k context window are the most relevant to making the decision of like what is the next tool we call and doing that over and over again in a loop.
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Their words
I think context engineering has been so long lived because it's it's grounded in the fundamentals of how transformer attention works and until we have post transformer models or linear attention or whatever it is which who knows when that's going to happen context engineering will be interesting and important to anyone building on AI
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Their words
And at the end of the day, they're all like different ways to pass tokens into a model and ask it to produce usually some structured output. And understanding that is a lot more powerful than trying to learn memory and trying to pick some agent framework off the shelf and some memory framework off the shelf.