LLM Logging

LLM  logging refers to the systematic recording of interactions, outputs, and other relevant data generated by language models like GPT during their operation. This process captures input prompts, model responses, usage patterns, performance metrics, and any errors or anomalies that occur. LLM logging is crucial for debugging, auditing, and improving the model's performance. It helps in tracking how the model is used, identifying issues such as biases or inappropriate content, and ensuring compliance with ethical guidelines.