This section discusses the growing importance of observability for applications powered by large language models (LLMs). AI-driven systems introduce new operational challenges, including unpredictable outputs, latency variability, and dependency on external models or APIs. Observability tools help teams monitor model performance, track prompts and responses, and identify issues such as hallucinations or degraded response quality. By collecting metrics, logs, and traces related to model interactions, engineering teams gain visibility into how AI systems behave in production. This insight allows organizations to improve reliability, optimize performance, and maintain trust in AI-powered applications as they scale.

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