Rationale for the operation of the reflective mechanism and quality enhancement effects
LLManager's reflection mechanism is a closed-loop learning system that works on three levels:
1. Trigger conditions
- superficial reflection: activate the explanation_reflection node when the manual only modifies the explanation text (correct answer but wrong reasoning)
- reflect in depth: trigger full_reflection node for full analysis when both answer and description are modified
2. Processing
- The system compares the points of difference between AI output and manual corrections
- Analyze error types (e.g., rule misinterpretation/contextual omissions) using specific prompt templates
- Generate reflective reports containing error attribution and suggestions for improvement
- Deposit the report in the proprietary reflective knowledge base
3. Quality improvement performance
| norm | Improved effectiveness |
|---|---|
| First round accuracy | Lift 40-60% (based on historical data) |
| Artificial modification rate | Weekly decline 15-20% |
| processing time | Reduced audit time by an average of 30% |
Actual Case: In the budget approval scenario of an enterprise, after 2 months of reflection mechanism optimization, the AI suggestion adoption rate has increased from 58% to 89%, and the accuracy rate of anomalous application identification has reached 92%.
This answer comes from the articleLLManager: a management tool that combines intelligent automated process approvals with human reviewsThe
May not be reproduced without permission:AI productivity tools " How exactly does LLManager's reflection mechanism work? How does it materially help to improve the quality of approvals?
