Logging prompts and responses from Large Language Models (LLMs) presents a trade-off between debuggability and data privacy. While not logging anything offers simplicity and privacy, it hinders bug reproduction and improvement. Logging only metadata provides insights into performance metrics like cost and latency but offers no context for output errors. The author advocates for a strategy of full text logging with redaction, short retention periods, and restricted access, which allows for debugging and eval set creation while mitigating privacy risks. AI
IMPACT Establishes best practices for LLM application development, impacting how developers manage data privacy and model debugging.
RANK_REASON The item discusses best practices and opinions on LLM logging, rather than announcing a new product or research.
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