Feedback Loop Automation for AI Assistants
Negative feedback is a gift if you can process it quickly. Manual spreadsheets lag and hide trends. Automations keep quality high even as query volume grows.
1. Collect consistent signals
- Provide thumbs up/down, optional free text, and a dropdown for reason codes.
- Capture the user question, assistant answer, citation URLs, retrieval scores, and fallback_reason.
- Record tenant_id, embed_id, page_path, language, and timestamp so you can segment.
2. Build a feedback pipeline
| Stage | Tooling | Output |
|---|---|---|
| Ingest | Webhook or message queue | Raw feedback events |
| Normalize | Worker that enriches events with crawl_version, prompt_version | Structured records |
| Store | DB table or warehouse | Queryable feedback queue |
| Notify | Google Chat/Pager, email, dashboards | Actionable alerts |
3. Routing logic
- Incorrect/outdated: Route to documentation owner with citations and retrieval context.
- Off-scope: Check threshold settings; consider raising the relevance floor or adding more crawl coverage.
- Provider error/timeout: Escalate to infra; compare with fallback dashboards.
- Compliance/security: Immediately alert the on-call channel with transcript links.
4. Automation tips
- Deduplicate near-identical feedback using question + citation hash.
- Auto-close feedback when crawls complete or prompt versions change; log resolution notes.
- Surface top unanswered questions and unresolved feedback in the admin UI.
5. Reporting
- Weekly summary: count feedback by reason, resolution time, and tenant.
- SLA target: respond to high severity within 4 hours, low severity within 2 business days.
- Export anonymized feedback to product teams for roadmap prioritization.
CrawlBot workflow
CrawlBot already pipes feedback into analytics, attaches retrieval traces, and triggers Google Chat alerts for negative flags. Adopt similar patterns if you build in-house; automation keeps AI quality scalable.***