Connect each comment to the version being assessed
Identify the exact output that received feedback, including its version, affected part and request context. A comment detached from the piece can lose meaning when the work changes. Preserve the original and correction so another person can understand the problem without having to guess what the previous state looked like or which option was selected.
“I preferred the first one” seems clear while every option is open on screen. Days later, it may be impossible to tell whether the person meant the title, image or presentation order. The record needs to recover that choice without making memory of the conversation the only available source.
Imagine a fictional company reviewing campaign copy. “This opening is too long” refers to an advertisement's initial version. If the text is replaced without retaining its reference, a later review may apply the criticism to the new opening even though the original problem has already been corrected in that version.
Keep the comment as it was made and add an operational interpretation separately. The original sentence shows the person's decision; the interpretation explains how the team intends to execute it. If uncertainty arises, you can return to the actual request instead of treating the assistant's reading as an explicit instruction.
This organisation is part of giving AI context without collecting information indiscriminately. You need not include the entire history in every task. You need a current reference indicating what applies and an accessible record explaining the origin of decisions relevant to the work now being prepared.
Define the assessment status too. “Needs changes” does not mean rejecting the whole idea, and “approved” may refer only to reviewed copy. Make the decision's object clear so partial approval cannot be used as authorisation to proceed with other parts of the work that were not reviewed or agreed.
To prepare your next step, explore AI assistants for internal knowledge.
Extract the criterion without inventing a broader rule
Translate the comment into an observable condition: when it applies, what should change and how to recognise the expected result. Keep that condition close to the person's stated reason. When feedback provides no reason, clarify the point that changes execution instead of filling the gap with a preference presumed by the assistant from limited information.
In the campaign example, “opening too long” may mean the main idea appears late. The reusable criterion would be to present that idea earlier when the format requires quick reading. It does not imply prohibiting all long paragraphs or setting a universal word limit for every company communication.
A factual correction needs different treatment. If the piece presents an offer that no longer exists, the rule may require checking the current catalogue before writing. The lesson concerns information validation, rather than merely substituting an expression in that particular advertisement and assuming the underlying issue has been resolved.
Distinguish taste, requirements and authorisation as well. Someone may like a composition without authorising publication. They may request a colour for one piece without changing the entire brand identity. The assistant should preserve those distinctions so the feedback record does not silently expand the scope of the original request.
When preparing advertising variations with AI, identify what the assessment selected. It could be the argument, clarity or fit with the audience. Reusing the criterion should enable new alternatives rather than require copying the approved sentence into every subsequent advertisement regardless of its objective or context.
If a comment contains several changes, separate them into understandable decisions. A note about a title, promise and image may require three corrections with different reasons. Separating them helps verify the output and avoids declaring completion when only the most visible part of the request has been carried out.
Define scope and resolve conflicts between decisions
Specify whether a criterion applies to one piece, a campaign, a client or all future brand work. Record who confirmed it and when it replaces earlier guidance. Without scope, a library can accumulate apparently incompatible rules that were actually approved for different situations, each with its own objective and reason for the chosen approach to execution.
The fictional company may want brief advertising and detailed explanations in proposals. Both decisions can coexist if the record identifies each format's purpose. Conflict appears when a local preference becomes a general rule and starts limiting tasks that require a different approach to serve their readers effectively.
If an earlier rule is explicitly corrected, update the reference used for current work. Preserve history to explain the change, but make the valid instruction clear. Two versions with equal standing force both the team and assistant to choose without knowing which reflects the current decision of the responsible person.
Repetition does not always authorise generalisation. Several rejections may have different reasons even if they result in the same visual or textual change. The assistant can suggest a common rule, but the responsible person should confirm whether that interpretation truly represents the preference they want to retain across future work.
The ability to explain why automation changed a campaign depends on this connection between rule and action. The record should show which criterion was in force and how it was applied. An old decision taken out of context cannot justify a change nobody authorised within that scope.
Keep the current reference short and organised by task. History can retain the details while execution instructions present applicable criteria and relevant exceptions. The intention is to make the next decision easier without turning every request into a requirement to read all previous conversations from the entire project.
Test the rule and retain what its application teaches
Apply the criterion to a new request and check whether it produces the expected behaviour without harming parts that already worked. Use cases inside and outside its scope to verify the distinction. The test should establish whether the rule is understandable and useful rather than merely confirm the assistant can repeat the wording stored in its instructions.
If the criterion requires the main idea earlier in a short advertisement, check a new piece in that format. Then inspect a longer explanatory text where the same limit may not apply. A better-defined rule adapts the decision to context instead of indiscriminately blocking other legitimate forms of communication.
Watch for unexpected effects too. An instruction to reduce text may remove a necessary condition; an instruction to add examples may generate invented details. Reusable feedback should support better choices while retaining the fidelity, clarity and suitability criteria already required of the original work before the new correction was added.
Record the application result in a short note. If the rule worked, save an example showing why. If it needed adjustment, explain what was too broad or unclear. Learning then includes the criterion's boundary instead of collecting only confirmations of the first interpretation and overlooking situations where it failed.
This process organises instructions and context; it does not mean the model has been retrained or will remember everything in every tool. Confirm that relevant criteria are available to the current work. A well-kept record helps only when the right information reaches the task that actually needs it.
Feedback gains value when it leaves an understandable decision for the next execution. Keeping the reason, version and scope reduces repeated discussion and makes corrections more consistent. AI can help organise that work provided it does not turn its own interpretations into rules attributed to the person who approved the piece.