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User correction, thumbs up/down, inline editing, and reinforcement signals.
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| name | feedback-loops |
| description | User correction, thumbs up/down, inline editing, and reinforcement signals. |
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.
The most valuable feedback is correction — but it's also the hardest to design for:
When to ask for feedback matters:
Feedback is only valuable if it changes something. The user needs to see that their feedback matters:
Proactively identifying failure modes, misuse, and unintended consequences.
Managing shared context, memory, and state across multiple agents.
Coordinating text, image, voice, and tool-use modalities in a single interaction.
Helping users form warranted trust in the AI — neither overtrust nor undertrust — through deliberate confidence and source signalling.
Reading user emotional state from text signals — caps, punctuation density, repetition, latency — and adapting before the user disengages.
Designing review workflows to surface and mitigate bias in AI outputs.
