Summary
Once the session ends, an AI agent forgets the feedback it has received, so when it comes to habits of writing or of judgment, the human side ends up repeating the same feedback over and over. To stop that repetition, this site is run on a mechanism where my feedback is written into the operating-rules documents as new rules, and the AI in the next session works according to them. In this article I actually count the operating records of the last two days of running this mechanism, and I write about where in an AI to put my own habits and values, and how to write them so that they take hold, and what comes out of that as a result.
Let me put the counted results first. My feedback remains as dated sections in a document called the record of instructions. When I counted the sections for the two days of July 10 and 11, 2026, at the point I began writing this article, there were 48. Of those, the feedback about how to write text was added, over 17 commits, to a file called the style skill that gathers the writing rules in one place, and at the same point that file held my own words, verbatim, as dated quotations in 11 places. The rules do not end once they are written; they became review points for the pre-publication check. Among the pre-publication checks, the ones that cannot be judged without reading the context are left to an AI, and I call this role the judge. When an already published article that dealt with search strategy was rewritten, this judge caught 6 places that needed fixing, and all of them were corrected. What is interesting is that my feedback becoming a rule, that rule becoming a pre-publication check, and the check catching the fix in the next article, all went around once within the same day I gave the feedback.
There were also failures where things did not take hold as taught. When I taught the value of writing readably, the AI translated it into numbers: an upper limit on the count of commas and on the length of a single sentence. It added that to the rules, but the rewrite that fit those numbers cut the sentences up too much, and the result was monotonous. Translating readability into numbers made the writing harder to read instead. I pointed out that this approach itself was an anti-pattern, threw away the numeric limits, and replaced them with a review that reads the draft aloud and looks at its structure.
In an earlier article, “Two projects, different in field and in build, had sorted where feedback to an AI should live into the very same three layers,” I dealt with the criterion for which layer to put a single piece of feedback in. This article is a continuation of that, the installment that deals with the whole act of conveying, that is, what I teach, where it takes hold, and what comes out of it. The body you can read with a subscription begins by showing, with the real figures from the two days, the premise that feedback disappears and the flow that carries feedback from the record into the rules. Next, I divide what is taught into four: the principles of values, the rules of writing, the yardstick for judgment, and the line you must not cross. I write with real examples that each takes hold in a different place. A written rule works only once it becomes a check, so I confirm that with the breakdown of the feedback the judge caught, and I write about the lesson in teaching that I got from the failure of numeric limits, and the caveat that whether things take hold cannot yet be measured, and I close with what came out of this work of conveying.
Audience and takeaways
This article is for people who want an AI agent to work in their own way, whether in coding or in writing, yet find themselves repeating the same instructions every time. You can take away the flow that carries a piece of feedback past the on-the-spot fix and into the rules. I also write about how to change the way you teach and where you place it depending on whether what you teach is a value or a procedure. A rule works only once the AI reads it, so I also cover how to connect the rules to a check and bring them into a form that does not depend on whether they are read. This article is a record of practice based on this site’s own operating records.
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