📅 2025-05-18 — Session: Debugged and Enhanced LLM Function Calls
🕒 05:45–06:25
🏷️ Labels: LLM, Debugging, Openai, Function_Call, Python
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to debug and enhance the function calling capabilities within the OpenAI API, focusing on improving reliability and error handling.
Key Activities
- Debugged issues with LLM’s function calling logic, ensuring correct triggering and error handling.
- Addressed common problems in OpenAI API function calls, focusing on prompt formatting and execution clarity.
- Enhanced Python logging for robust debugging of API calls, including detailed input/output logging.
- Analyzed and improved the
parsed_message
function, addressing issues with long prompts. - Reflected on structural and semantic differences in prompt engineering, and identified challenges in schema completion by LLMs.
- Diagnosed issues in LLM processing, suggesting improvements in prompt tokens and logging practices.
- Provided comprehensive Python code for enhanced observability in LLM call debugging.
Achievements
- Improved debugging techniques for OpenAI function calls.
- Developed enhanced logging and error handling strategies.
Pending Tasks
- Implement suggested improvements in prompt engineering and logging practices.
- Further testing of the enhanced debugging strategies in different scenarios.