Debugged and Optimized PromptFlow Execution

  • Day: 2025-04-30
  • Time: 06:50 to 08:10
  • Project: Dev
  • Workspace: WP 2: Operational
  • Status: Completed
  • Priority: MEDIUM
  • Assignee: Matías Nehuen Iglesias
  • Tags: Promptflow, Debugging, Logging, Optimization, Node Execution

Description

Session Goal

The session aimed to debug and optimize the execution of various nodes in a PromptFlow setup, focusing on improving logging, output declaration, and pipeline functionality.

Key Activities

  • Debugging Outputs: Explored methods for inspecting intermediate values in PromptFlow Python tools, moving beyond print() to more robust logging strategies.
  • Managing Artifacts: Reviewed key files and folders generated after a PromptFlow run to guide debugging efforts.
  • Flow Execution Summary: Analyzed successful flow execution, identified issues with truncated outputs, and recommended improvements in logging and JSON handling.
  • Execution Log Analysis: Investigated execution logs of my_python_tool, suggesting updates to logging and error handling.
  • Output Declaration Fixes: Addressed missing outputs in the filter_llm node by providing solutions for explicit output declarations.
  • YAML Configuration Correction: Provided a correct outputs: section for a flow YAML to ensure accurate node output references.
  • Node Execution and Optimization: Confirmed successful execution of filter_llm, filter_prompt, and match_prompt nodes, with recommendations for future debugging and prompt optimization.
  • Review Process Enhancements: Redesigned review agent prompts for strategic evaluation, incorporating executive perspectives.

Achievements

  • Successfully debugged and optimized multiple nodes within the PromptFlow, ensuring correct output declarations and improved logging.
  • Enhanced review agent prompts for strategic decision-making.

Pending Tasks

  • Further refine logging strategies to capture all necessary outputs in future runs.
  • Continue optimizing prompt structures for better clarity and efficiency in the match_llm function.

Evidence

  • source_file=2025-04-30.sessions.jsonl, line_number=3, event_count=0, session_id=8ad6e6d451739fe649204b50ddc9b44682427dfa1c49dede1dde0e480f1068b4
  • event_ids: []