Self-Learning Feedback

Purpose

Defaults

• GOOD_APPROVAL_TARGET = 10

• SLIGHT_MAX_ITERATIONS = 5

• MEDIUM_MAX_ITERATIONS = 10

• SEVERE_MAX_ITERATIONS = 20

GOOD_APPROVAL_TARGET

When To Trigger

• A discrete task was completed.
• A cron job execution and response was completed.

• Mid-task.
• During a multi-step process before a natural completion point.
• More than once for the same completion event.

Non-Intrusive Feedback Prompt

Task completed. Was this good or does it need improvement?

Feedback Branches

Branch A: User says "Good"

1. In the same turn, ask:

   What was good about this output?

2. Capture the user's positive signal (quality, format, style, speed, clarity, etc.).
3. Increment the consecutive good-approval counter for this task type/context (e.g. same cron job, same report type, same command). If the user had said "Needs improvement" on a previous task, the counter for that context was already reset—only consecutive "good" answers count toward the target.
4. Repeat this learning behavior on every future completion of the same task type until the counter reaches

   GOOD_APPROVAL_TARGET

   .
5. Once target is reached, treat pattern as learned and reduce prompt frequency:
   • Ask "what was good" only when task type changes, quality drops, or periodic recalibration is needed.
6. Counter reset: If the user says "Needs improvement" for this task type/context, reset the good-approval counter to 0 for that context so that the next "good" answers start a new consecutive run toward the target.

Branch B: User says "Needs improvement"

1. Collect improvement intent in three dimensions:
   • Why it needs improvement.
   • How it should be improved.
   • What context/styling should change.
2. Ask for examples if possible.
3. Classify severity and set iteration cap.
4. Resolve with web research and iterative reasoning.
5. Apply changes in the next similar output.
6. Ask feedback again after completion.

Multiple-Choice Collection Template

AskQuestion

Why It Needs Improvement

• Incorrect or incomplete result
• Wrong format or structure
• Too slow or inefficient
• Wrong tone or style
• Missing context or assumptions
• Other (user specifies)

How It Should Improve

• More detail/depth
• Less detail/more concise
• Different structure/order
• Different method/tooling
• Stronger validation or checks
• Other (user specifies)

What Context/Styling Should Improve

• Code style/conventions
• Output format (markdown, JSON, table, prose)
• Terminology/domain language
• Length/scope boundaries
• Audience/tone expectations
• Other (user specifies)

If you can share an example of the desired output/style, please provide one.

Severity Metric And Iteration Control

• Slight: minor wording/format/style adjustments -> max 5 iterations
• Medium: structural/depth/method changes -> max 10 iterations
• Severe: fundamental or repeated failures -> max 20 iterations

why + how + what + context

• If under cap: continue iterative refinement (apply → ask feedback again).
• If cap reached: summarize lessons learned, stop iterating on that issue, and continue normal feedback prompts (still ask "Good or needs improvement?" after subsequent tasks).

Why-How-What Resolution Requirement

• Why this change is needed.
• How it will be changed (concrete steps).
• What context/styling constraints are being applied.

UX Safety Rules

• Never spam feedback prompts.
• Never interrupt active user workflows for feedback.
• Keep prompts concise and predictable.
• Do not require UI or product changes; this is a conversational behavior layer.

Lightweight Output Pattern

1. Completion line.
2. Single feedback question.
3. Optional one follow-up block only if user chooses "Needs improvement."

Additional Resources

• Detailed question sets and example forms: reference.md