Quality Controls
Ensuring accuracy, relevance, and privacy in AI reasoning
Confidence Scoring
Transparent reliability metrics for every recommendation
High Confidence (90-100%)Well-established protocols
Medium Confidence (70-89%)Multiple valid approaches
Low Confidence (<70%)Experimental or uncertain
Confidence scores are calculated based on evidence quality, source recency, and consensus across multiple data sources.
Staleness Detection
Automatic flagging of outdated information
Staleness Thresholds:
- Critical: Safety protocols, contamination data (<1 year)
- Important: Cultivation techniques, equipment specs (<3 years)
- General: Species characteristics, basic biology (<5 years)
Warning: This recommendation uses data from 2019. Newer research may be available.
Reasoning Privacy
Protecting proprietary cultivation methods
Chain-of-thought reasoning can be hidden for sensitive commercial operations while still providing actionable recommendations.
Privacy Levels:
- Public: Full reasoning visible to all users
- Private: Reasoning hidden, only final recommendations shown
- Proprietary: Custom protocols not shared with knowledge base
Feedback Loops
Continuous learning from user outcomes
Users can rate recommendations and report outcomes, improving future responses.
Feedback Metrics:
✓ Was this recommendation helpful?
✓ Did you achieve the expected results?
✓ What was your actual yield/outcome?
✓ Any unexpected issues?
Feedback is aggregated and used to update confidence scores and refine protocols.