Developed and refined AI message parsing schema

📅 2025-05-04 — Session: Developed and refined AI message parsing schema

🕒 17:20–18:25
🏷️ Labels: Ai Parsing, Schema Design, Automation, Workflow Improvement
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The primary goal of this session was to develop and refine a schema for parsing AI-generated messages, focusing on enhancing the structure for better classification and reusability.

Key Activities

• Reviewed strategies for improving AI interactions through systematic log parsing and workflow enhancements.
• Outlined an initial parser plan for annotating AI messages, detailing preprocessing, structural classification, output chunking, and tagging phases.
• Proposed enhancements for parser design by identifying additional dimensions such as response category, target tools, and technical depth.
• Finalized the schema for Phase 1 Parser, detailing the structure and classification of parsed messages.
• Planned schema refinement based on new samples, suggesting hierarchical tagging, persona fields, and linked entities.
• Discussed the content_medium field’s role in content categorization for software development.
• Provided specifications for structural fields in message parsing.
• Defined a refined schema for ParsedMessage, facilitating retrieval, classification, and reuse.
• Executed heuristic time-based session segmentation and aggregated session data using Pandas.
• Planned and executed a reliable PromptFlow setup for processing chat history.

Achievements

• Successfully developed a comprehensive schema for AI message parsing, incorporating multiple dimensions and enhancements.
• Established a systematic approach for refining schemas based on real-world samples.
• Implemented practical methods for session segmentation and data aggregation.

Pending Tasks

• Further testing and validation of the refined schema in real-world scenarios.
• Implementation of the enhanced parser design in production environments.