📅 2025-05-18 — Session: Enhanced Debugging Techniques for OpenAI API
🕒 05:45–06:25
🏷️ Labels: Debugging, Openai Api, Prompt Engineering, Python, Error Handling
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to debug and enhance the function calling capabilities of the OpenAI API, focusing on improving prompt execution and error handling.
Key Activities
- Debugging Function Calls: Identified and fixed issues in the LLM’s function calling logic to ensure correct triggering and error handling.
- API Usage Optimization: Addressed common problems in OpenAI API function calls, focusing on prompt formatting and execution clarity.
- Enhanced Debugging Logs: Developed a robust Python code block for detailed logging of OpenAI API calls, improving error tracking.
- Prompt Engineering Analysis: Analyzed structural and semantic differences in prompts to enhance LLM output consistency.
- Technical Challenges: Explored reasons for LLM’s schema completion issues and proposed solutions to improve model performance.
- Diagnosis and Recommendations: Provided insights into cluster processing issues and suggested improvements in prompt and logging practices.
Achievements
- Successfully enhanced the debugging process for OpenAI API calls, improving reliability and observability.
- Developed actionable insights for prompt engineering and LLM processing, leading to better model performance.
Pending Tasks
- Further refine prompt structures based on session insights to optimize LLM output.
- Implement suggested improvements in logging practices to ensure comprehensive error tracking.