Understanding the Layers of AI Observability in the Age of LLMs

AI observability has become crucial for understanding and monitoring large language models (LLMs) and generative AI systems, which are inherently probabilistic and lack transparent execution paths. Unlike traditional software, these models operate as "black boxes," making it challenging to trace decision-making processes, especially in high-stakes environments, thereby necessitating advanced observability techniques similar to logging, metrics, and distributed tracing used in conventional software engineering. To address these challenges, a layered approach to AI observability is emerging, where each stage of an AI pipelinesuch as input processing, model response, and downstream actionsis monitored