Put feedback where it can do its job: session-wide instructions belong in rules, repeatable workflows belong in skills, and the record of past decisions belongs in memory. This three-layer framework is described by matsumotory in a later follow-up to a September 25 post; the follow-up says two projects with different fields and builds had arrived at the same way of sorting feedback. It does not establish that the same setup works for every AI system, but it offers a practical answer to a common question: how do you make an AI carry useful feedback into future work?
What are the three places feedback can live?
Matsumotory summarizes the framework in a July 11, 2026 follow-up: rules documents are read each session, skills gather fixed procedures, and memory keeps a history of decisions. The distinction is about the role of the information, not simply its length or importance.
| Layer | Best fit | How it helps |
|---|---|---|
| Rules documents | Instructions that should guide work every session | Put durable operating expectations where the AI is instructed to consult them. |
| Skills | Reusable procedures for a recurring kind of task | Keep a defined workflow together so it can be applied consistently when relevant. |
| Memory | The history and context behind decisions | Preserve what happened and why, without treating every past event as a standing rule. |
The follow-up does not document the original September 25 post’s detailed examples or assignment criteria, so the table expresses the later summary rather than claiming to reproduce the original post’s full argument. Matsumotory’s July 11 follow-up
How should feedback move from a correction to future practice?
A useful workflow begins by recording the feedback in context, then promotes only the parts that should shape future work. Matsumotory describes keeping feedback first in a dated instruction record, turning reusable lessons into rules or a skill, and correcting relevant published work. In this account, the record preserves the decision’s history; the rule or skill makes a generalized lesson available for later tasks.
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- Record the feedback with its date and context. Capture what was corrected and the relevant decision history, rather than silently turning a one-off comment into a universal instruction.
- Decide whether it generalizes. If it should apply to future work generally, express it as an operating rule. If it describes a repeatable procedure for a particular task, put it in a skill. If it mainly explains what happened or why a choice was made, retain it as memory.
- Apply the correction to existing work when appropriate. Matsumotory includes correcting relevant published material in the described workflow; feedback storage alone does not repair earlier output.
- Make rules reviewable. The follow-up describes translating rules into review criteria, using an AI reviewer for some checks and automatic checks only for a narrower group of mechanically clear prohibitions.
A written rule can only influence output if the system reads and applies it. Connecting rules to review criteria makes them easier to act on, but matsumotory presents this as a personal workflow, not a controlled evaluation or proof that it generalizes across AI products.
Which kinds of instruction need different treatment?
The follow-up further separates four instruction types. This is a later elaboration of the three-layer framework, not a verified structure from the September 25 article.
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- Values: Broad priorities that sit above individual style rules and help resolve trade-offs.
- Writing habits: Concrete, repeatable practices that can be stated as operating rules.
- Judgment yardsticks: Criteria that help the AI handle new cases instead of merely avoiding a list of banned words.
- Publication boundaries: Non-negotiable limits that should act as a stop condition when a draft crosses them.
These categories are useful because not all feedback should become a prohibition. A specific correction may be a one-time decision; a recurring procedure may belong in a skill; and a principle may guide judgment without being reducible to a mechanical test.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should be automated, and what should remain a judgment call?
Matsumotory describes trying numeric readability limits for commas and sentence length, then removing them after the prose became choppy. The follow-up’s practical distinction is to automate checks with clear mechanical outcomes and leave contextual matters such as values and readability to review. A rule that sounds precise can still produce poor writing if it is applied without regard to context.
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In the author’s own two-day workflow, July 10–11, 2026, matsumotory reports 48 instruction-record sections (32 dated July 10 and 16 dated July 11), 17 commits to the style skill (7 and 10 respectively), and 6 issues found in a rewrite check of an already published search-strategy article. The author also reports 8 review points for an AI judge and 4 machine-checked prohibitions. These are counts from one author’s workflow, not general performance statistics. The author says there was not yet a yardstick to measure whether recurring feedback had decreased. Read the follow-up and its account of the workflow.
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