Feedback capture collects structured human input on AI outputs to enable learning and quality improvement. It turns user reactions like thumbs up, corrections, and ratings into data that identifies patterns in AI failures. Without feedback capture, AI systems repeat the same mistakes because they never learn what users actually need.
Users say the AI is "wrong" but cannot explain how.
You fix one complaint, break something else.
There is no way to know if the AI is getting better or worse over time.
Without structured feedback, improvement is just guessing.
HUMAN INTERFACE LAYER - Turning user input into AI improvement.
Feedback capture systematically collects human reactions to AI outputs. This includes explicit signals like ratings and corrections, and implicit signals like whether users accepted or edited a suggestion. The goal is creating data that reveals patterns in AI performance.
Unlike random complaints in Slack or support tickets, structured feedback is categorized, timestamped, and linked to specific outputs. This makes it possible to aggregate patterns, measure trends, and prioritize improvements based on actual user experience.
Good feedback capture requires almost no effort from users but provides rich signal for improvement.
Feedback capture solves a universal challenge: how do you know if something is working? The same pattern applies anywhere you need to measure quality through human judgment.
Present work for evaluation. Capture the judgment with minimal friction. Categorize the signal. Aggregate to find patterns. Use patterns to prioritize improvements.
Imagine you are a user evaluating an AI support assistant. Rate each response, and watch how structured feedback reveals hidden problems.
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Thumbs up/down, helpful/not helpful
The lowest-friction option. Users tap once to signal good or bad. High response rates but limited detail. Best for high-volume interactions where you need quantity over depth.
Rate accuracy, relevance, tone separately
Users rate multiple aspects of the output. Reveals which dimensions are failing. More friction than binary but provides richer signal for targeted improvements.
Users fix the output directly
Instead of rating, users edit the AI output to make it correct. The diff between AI output and user correction is pure gold for training. Highest effort but richest signal.
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The team receives scattered complaints about AI quality but cannot find patterns. They implement structured feedback capture: thumbs up/down on every response with optional categorization. After a week, they discover 70% of negative feedback is about one specific topic - something they can actually fix.
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This component works the same way across every business. Explore how it applies to different situations.
Notice how the core pattern remains consistent while the specific details change
Every AI response ends with a 5-question survey. Users stop responding. The feedback you get is from unusually frustrated or delighted users, skewing your understanding of actual performance.
Instead: Make the default feedback action a single tap. Offer optional detail for users who want to explain more. Capture implicit signals that require no effort.
You have thousands of "thumbs down" signals but no idea why. Is the AI inaccurate? Too verbose? Wrong tone? Without categories, you cannot prioritize what to fix first.
Instead: Add one optional follow-up: "What was wrong? Accuracy / Relevance / Tone / Other." Even 20% categorization rate gives you actionable patterns.
You know which outputs got negative feedback but not what input produced them. You cannot reproduce the failure or understand the pattern. The feedback is noise.
Instead: Store the complete context with every feedback signal: the input, the output, the user, the timestamp, and any relevant metadata. Make reproduction easy.
Feedback capture is the systematic collection of human input on AI outputs. This includes explicit signals like thumbs up/down ratings, corrections to AI responses, and quality scores. It also includes implicit signals like whether users accepted a suggestion or immediately edited it. The goal is to create a structured dataset that reveals patterns in AI performance.
AI systems cannot improve without knowing what works and what fails. Feedback capture creates the data needed to identify failure patterns, prioritize fixes, and measure improvement over time. Without it, teams rely on random complaints rather than systematic quality measurement. Feedback capture transforms anecdotal observations into actionable metrics.
Collect both explicit and implicit feedback. Explicit includes ratings, corrections, and reported issues. Implicit includes whether users accepted suggestions, how long they spent reviewing output, and whether they requested regeneration. The best systems capture structured categories like accuracy, relevance, and tone rather than just binary good/bad signals.
Make feedback low-friction. A single tap thumbs up/down captures 80% of the signal. Offer optional detail fields for users who want to explain. Place feedback prompts at natural decision points, not as interruptions. Track implicit signals that require no user effort. Avoid overwhelming users with surveys after every interaction.
Aggregate feedback to find patterns, not just individual complaints. Categorize negative feedback by failure type: accuracy, relevance, tone, format. Prioritize fixes by frequency and severity. Use positive feedback examples as training data or few-shot examples. Create feedback loops where improvements are measured against the same feedback metrics.
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Choose the path that matches your current situation
You have no feedback collection on your AI outputs
You collect ratings but struggle to act on them
You have categorized feedback and want to close the loop
You have learned how to collect structured feedback on AI outputs. The natural next step is understanding how to turn that feedback into systematic evaluation and improvement.