📅 2025-04-24 — Session: Enhanced Automation with Modular Flow Designs

🕒 18:20–19:40
🏷️ Labels: Automation, Promptflow, DAG, README, Python
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session aimed to enhance automation processes by designing and implementing modular flow systems using various scripting and orchestration tools.

Key Activities

  • Crafted JSONL Entries: Developed JSONL entries for defining high-leverage meta-flows, focusing on automation and orchestration.
  • Standardized Input Schema: Synchronized naming and structure across data processing flows, including defining a unified input schema and updating DAG configurations.
  • Designed Dynamic Folder Analysis: Created a refined approach for folder-based flow design, enhancing modularity and reusability.
  • YAML DAG for README Generation: Implemented a YAML-based DAG for generating README files using Azure ML’s prompt flow.
  • Script Development: Developed Python scripts for reading folder files and generating README.md files, ensuring robust file handling and clean formatting.
  • Output References Fix: Addressed issues with output references in PromptFlow, ensuring explicit declaration of output keys.
  • README Audit and Update: Conducted a thorough audit and update of README documentation to align with actual flow designs.
  • Self-Healing Packaging System: Conceptualized a self-healing system for automating the detection and repair of flow inconsistencies.

Achievements

  • Successfully implemented modular and dynamic flow designs that enhance automation efficiency.
  • Improved documentation accuracy and usability for future users.

Pending Tasks

  • Further testing and refinement of the self-healing packaging system to ensure robustness.
  • Continued development of modular flow fixer pipeline using DAG architecture.