Optimized Feedback Loop and Annotation Strategy

📅 2025-05-11 — Session: Optimized Feedback Loop and Annotation Strategy

🕒 18:15–19:20
🏷️ Labels: Data Analysis, Annotation, Taxonomy, Feedback Loop
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session aimed to optimize feedback loop mechanisms and improve annotation fidelity within 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 overuse of the ‘insight’ category in annotations, proposing targeted improvements for better classification accuracy.
• Developed a structured definition for the note_type field to enhance note organization and retrieval.
• Addressed taxonomy drift in category fields, recommending strategic category collapses for improved data organization.
• Refined message type classification to better capture communicative intent and improve data categorization.
• Proposed a refined definition of ‘actionable’ in task management to prioritize explicit action items.

Achievements

• Clarified the need for schema enforcement in feedback loop datasets.
• Identified key improvements for annotation strategies to enhance usability.
• Established a semi-hierarchical taxonomy for note types.
• Provided strategic recommendations for managing taxonomy drift and improving message classification.

Pending Tasks

• Implement the proposed schema enforcement for feedback loop datasets.
• Develop a detailed plan for the refined annotation strategy.
• Execute the taxonomy adjustments and message classification refinements.