About feedback-loops
A design skill for AI product teams creating feedback mechanisms that help AI adapt instead of repeating mistakes. It covers feedback types, correction flows, timing, and how feedback should lead to visible changes.
Key features
- 01Explicit and implicit feedback
- 02Inline correction and partial acceptance
- 03Feedback timing guidance
- 04Feedback-to-adaptation mapping
- 05Feedback mechanism specifications
Use cases
- 01Designing edits and regeneration as feedback signals
- 02Creating flows for correcting part of an AI response
- 03Choosing when to request ratings
- 04Defining how feedback changes current or future responses
Install
Copy the command and run it in a terminal.
$
npx skills add https:/After installing, type/feedback-loopsin Claude Code to confirm it loaded.
Details
From the project READMEFeedback Loops
Feedback loops are how users tell the AI what's working and what isn't. Designing these loops well is the difference between an AI that improves over time and one that repeats the same mistakes.
Types of Feedback
- Explicit feedback: Thumbs up/down, star ratings, "this was helpful/not helpful" buttons
- Implicit feedback: Regeneration (user asks again), editing (user modifies the output), abandonment (user leaves)
- Corrective feedback: User provides the right answer ("No, I meant X not Y")
- Preference feedback: User chooses between alternatives ("I prefer option B")
- Contextual feedback: Feedback tied to a specific part of the output, not the whole response
Designing for Correction
The most valuable feedback is correction — but it's also the hardest to design for:
- Inline editing: Let users edit AI output directly. Track what they change.
- Partial acceptance: Let users keep some parts and reject others.
- Explanation requests: "Why did you do it this way?" — the user's question reveals what went wrong.
- Redo with guidance: "Try again but make it more formal" — correction through re-prompting.
Feedback Timing
When to ask for feedback matters:
- Too early: User hasn't evaluated the output yet. Feedback is premature.
- Too late: User has moved on. The moment for feedback has passed.
- Interruptive: Modal dialogs or required ratings break flow.
- Ambient: Passive signals (edits, regeneration) collected without asking. Design for ambient feedback first. Add explicit feedback sparingly.
Closing the Loop
Feedback is only valuable if it changes something. The user needs to see that their feedback matters:
- Immediate adaptation: The AI adjusts in the current conversation
- Persistent learning: The AI remembers preferences across sessions
- Acknowledgment: "I'll keep that in mind" — even if adaptation is delayed
Design Artefacts
- Feedback mechanism inventory per feature
- Implicit signal definitions (what counts as positive/negative)
- Feedback-to-adaptation mapping (what changes based on what feedback)
- Correction flow specifications
Comments
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