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.
Related Terms
More ai/ml Terms
Retrieval-Augmented Generation (RAG)
An AI architecture that combines information retrieval with text generation to produce answers grounded in source documents.
Vector Embedding
A numerical representation of text as a high-dimensional vector, enabling semantic similarity comparisons between passages.
BM25
A probabilistic keyword-ranking algorithm that scores documents by term frequency and inverse document frequency.
Chunking
The process of splitting large documents into smaller, overlapping segments optimized for retrieval and embedding.
Hallucination
When an AI model generates plausible-sounding but factually incorrect or fabricated information.
Large Language Model (LLM)
A neural network trained on massive text corpora that can understand and generate human language.
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