Artificial intelligence (AI) observability refers to the ability to understand, monitor, and evaluate AI systems by tracking their unique metrics—such as token usage, response quality, latency, and model drift. Unlike traditional software, large language models (LLMs) and other generative AI applications are probabilistic in nature. They do not follow fixed, transparent execution paths, which makes
The post Understanding the Layers of AI Observability in the Age of LLMs appeared first on MarkTechPost. Read More
