📅 2025-05-11 — Session: Optimized Feedback Loop and Annotation Strategies
🕒 18:15–19:20
🏷️ Labels: Feedback Loop, Annotation, Taxonomy, Classification, Data Analysis
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to optimize feedback loops through dataset analysis and improve annotation strategies in AI systems.
Key Activities
- Conducted exploratory data analysis on a dataset with 2,037 annotated responses to evaluate field consistency and potential schema enforcement.
- Analyzed the overuse of the ‘insight’ category in annotations, suggesting improvements for classification accuracy.
- Defined a structured taxonomy for the
note_typefield to enhance note organization and retrieval. - Addressed taxonomy drift in category fields, recommending strategic category consolidation.
- Improved summary guidelines to enhance semantic value and consistency.
- Proposed a nuanced approach to message type classification to better understand communicative intent.
- Refined the definition of ‘actionable’ in task management to prioritize explicit action items.
- Evaluated domain field tagging issues, recommending canonical mapping and normalization.
Achievements
- Developed strategic recommendations for feedback loop optimization and annotation fidelity.
- Established a semi-hierarchical taxonomy for better note classification.
- Identified and addressed taxonomy drift, improving data organization.
Pending Tasks
- Implement the proposed schema enforcement and taxonomy recommendations.
- Further refine message type classification and actionable task definitions for better workflow integration.