engineering · 1 min read
Feedback Loops: Turning User Signal Into Better Prompts
Thumbs-up / thumbs-down is data, not noise. Here is how Bhogar AI turns feedback signal into prompt improvements without overfitting to vocal users.
BABhogar AI TeamProduct & Engineering
User feedback - thumbs, ratings, edits - is the highest-value signal you have for improving an AI feature. Most teams collect it and never act on it.
Why it matters
The risk of acting naively is overfitting to a vocal minority. The right design weights feedback by user segment, samples for hand-review and feeds verified samples into the eval set.
How Bhogar AI approaches it
Bhogar AI ships a feedback pipeline: capture per-output feedback, segment by user cohort, sample for review, promote to eval dataset, and trigger prompt improvement workflows.
- Per-output feedback capture API
- Cohort-weighted aggregation
- Sample-for-review queue
- Promotion to versioned eval dataset
- Optional prompt-improvement workflow trigger
What you get
Teams running structured feedback loops improve perceived quality measurably quarter-over-quarter without overfitting to noisy users.