Customer Feedback Loop Design

Customer feedback on AI outputs is high-value signal. Design the collection mechanism so users provide feedback naturally; capture it cleanly; use it to improve the product. Three UI patterns work; the underlying infrastructure is straightforward.

UI patterns

• Thumbs up/down: simplest; ~5-15% engagement rate; binary signal
• 1-5 rating: more nuanced; ~3-8% engagement; multi-level signal
• Free-text comment: low engagement (~1-3%) but highest information density
• Edit-to-final: highest signal; user's actual ideal output. Indirect; requires UX support for edit-then-submit flow.

Layered approach is best: thumbs always, optional rating + comment, edit captured automatically when user edits AI output.

Infrastructure

• Per-response feedback table: response_id, feedback_type, value, timestamp, user_id
• Linked to original response: the prompt + retrieved context + AI output that received feedback
• Tenant scoping: per-tenant feedback corpus for tenant-specific fine-tunes
• Privacy: anonymise / redact PII before using for training

Uses

• Eval harness curation: low-rated cases become eval examples
• Fine-tuning data: high-rated outputs become SFT examples; low-rated → rejected in DPO pairs
• Prompt iteration: pattern-match on consistently low-rated cases; tune prompt to address
• Model selection: when a new model has higher rating distribution, that's strong signal
• Cohort analysis: per-tenant / per-feature / per-language quality breakdown

Verdict

Customer feedback infrastructure is the foundation of continuous AI quality improvement. Build day-one; integrate into product UX; use the data systematically. Self-hosted enables actual use of feedback for fine-tuning; hosted APIs leave the feedback "trapped" in your logs without ability to improve the model.