User-Driven System Refinement
Enterprise AI models require continuous calibration based on actual operational usage. AidEun captures structured feedback from internal users to identify knowledge gaps, refine retrieval accuracy, and optimize workspace performance over time.
Systematic Quality Measurement
Transform daily employee interactions into actionable system improvements:
- Implicit & Explicit Feedback: Capture thumbs-up/down ratings, detailed revision notes, and response copy actions to measure workspace utility.
- Knowledge Gap Analysis: Identify recurring queries where internal knowledge bases lack sufficient documentation, highlighting areas for corporate knowledge base updates.
- Benchmark Auditing: Periodically evaluate assistant response accuracy against verified ground-truth document datasets.
Refinement Infrastructure
Closed-Loop Calibration
- Retrieval Adjustment: Fine-tune document ranking algorithms dynamically based on feedback patterns.
- Prompt Optimization Logs: Provide prompt engineers with quantitative performance data to refine system instructions systematically.
