| name | designing-ai-feedback-control |
| description | Use when designing ShotgunCV AI feedback, correction, override, retry, reset, preference, or human-in-the-loop controls. |
Designing AI Feedback Control
Overview
ShotgunCV feedback controls should keep the user in charge of local artifacts and repeated job-application decisions. Feedback improves the current run workflow first; it should not imply model training, remote sync, or hidden automation.
Feedback Model
| Feedback type | Best UI |
|---|
| Fast quality signal | Useful/not useful, approve/reject, compact reason menu. |
| Correction | Inline edit, field-level correction, replacement text, evidence override note. |
| Workflow decision | Accept recommendation, skip, defer, compare alternatives. |
| Local preference | Remember locally, stop suggesting this, reset preference. |
| Failure report | Wrong source, missing evidence, hallucinated claim, unsupported rewrite, unsafe suggestion. |
Control Rules
- Say what feedback affects: this view, this run, future local runs, or nothing persisted yet.
- Keep manual fallback available for resume edits, JD prioritization, and final delivery decisions.
- Put undo, revert, and compare near generated edits.
- Let users retry with changed inputs or selected artifacts instead of only "regenerate".
- Make opt-out and reset discoverable for persistent local preferences.
- Never overwrite user edits or
run_dir artifacts without an explicit confirmation path.
ShotgunCV Fit
- Generated resume variants need accept, edit, revert, compare, and mark unsupported.
- Scorecards need dimension-level correction notes, not only global thumbs.
- Ranking explanations need "evidence is wrong/missing" feedback.
- Strategy plans need defer/skip decisions so users can keep the pipeline moving.
- Run actions need clear separation between UI-only state, local status writeback, and Python pipeline execution.
Copy Rules
- Use Chinese action text such as "仅更新当前视图", "写入当前 run", "保留人工处理", and "重试并保留原稿".
- Avoid vague thanks-only feedback. Tell the user what changed.
- Avoid implying model training unless the implementation actually trains or tunes.
- Avoid user personas, team roles, or account language that conflicts with the single-user local boundary.
Common Mistakes
- Asking for feedback after the user has already left the task.
- Treating all negative feedback as the same signal.
- Letting regenerate overwrite user edits without confirmation.
- Hiding the non-AI path after introducing automation.
- Saving preferences in browser storage, logs, or artifacts without saying so.
Checklist
References
