Quality loop
Answers improve through review
Test AI answers before changing settings. Use conversation feedback and unanswered searches to improve your knowledge, review drafts, and check that saved content is indexed.
Approved knowledge
AI response quality starts with enabled articles, approved policy wording, and source boundaries operators can inspect.
- Enabled articles
- Source boundaries
- Revision history
Feedback and misses
Thumbs-down answers, unresolved intents, top unanswered searches, and stale-knowledge misses become review inputs for the team.
- AI feedback
- Unanswered searches
- Knowledge gaps
Cost and prompt review
Usage review separates model spend, prompt waste, knowledge gaps, and tool-call risk before pricing or ROI claims are made.
- Usage export
- Prompt waste
- Provider cost
Review signals beat unsupported self-learning claims.
| Capability | Status | Detail |
|---|---|---|
| Multi-turn sandbox | Available | Test follow-up questions using recent conversation context without creating a live visitor conversation. |
| Candidate settings review | Available | Compare model or instruction changes with your saved settings, inspect the result, then explicitly load and save a candidate. |
| Knowledge index status | Available | Inspect article coverage and queued indexing jobs. Request a rebuild of saved knowledge when an embedding provider is configured. |
| Reviewable gap drafts | Available | Create or reopen a draft linked to an unanswered search. Drafts stay disabled for AI and the gap stays open until your team explicitly resolves it. |
| Knowledge base articles | Available | Enabled articles can ground AI context while article snapshots preserve what changed over time. |
| Article feedback | Available | Visitor and operator feedback can identify stale, confusing, or missing article coverage. |
| Top unanswered searches | Available | Unanswered help-center searches become content-gap signals for future articles and policy fixes. |
| Knowledge revision packets | Available | Revision packets support human drafting, rollback review, and procurement-friendly change evidence. |
| AI response feedback | Available | Feedback on AI answers helps prioritize prompt, article, and escalation review without claiming verified resolution learning. |
| Notion, Google Docs, YouTube, OCR and Q&A sources | Available | Notion syncs pages shared with your integration (weekly re-sync with auto re-crawl on); Google Docs, Sheets and Slides import by share link; YouTube imports public captions; scanned PDFs and images are read with OCR; Q&A pairs pin exact answers. |
| Other ingestion connectors | Not claimed | No private Google Drive folders, Dropbox or Confluence. Do not claim them until they ship. |
| Self-learning automation | Not claimed | ZChat should not claim autonomous retraining, automated prompt rollback, or verified outcome learning from review signals alone. |
The quality story is deliberately human-reviewed: use signals to improve approved knowledge before expanding automation.