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LLM Observability

The practice of monitoring, logging, and analyzing the inputs, outputs, and behavior of large language models in production systems.

LLM observability applies the principles of traditional software observability (metrics, logs, traces) to AI systems. Key signals include: token usage and cost per query, latency distributions, retrieval quality metrics, answer quality evaluations, user feedback, and error rates. Observability platforms like LangSmith, Langfuse, and Helicone capture the full context of each LLM call — the system prompt, retrieved passages, generated response, and any tool calls — enabling debugging, quality monitoring, and cost optimization.

For document intelligence applications in regulated industries, observability serves a compliance function as well as a technical one. Organizations may need to maintain logs of what questions were asked, which documents were retrieved, and what answers were generated — both for audit purposes and to investigate any instances where AI-generated analysis led to an incorrect business decision. Retention of LLM interaction logs should be considered as part of the broader data governance framework.

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