Users of large language models (LLMs) need to be confident in the safety, security, performance, trustworthiness and usefulness of the insights. In addition, it is often challenging to uncover hidden issues in agentic workflows. To address these concerns, IT, data engineering teams and developers can turn to LLM observability to diagnose and address issues concerning quality, safety, correctness and performance.
Let’s discuss the differences between LLM observability and LLM monitoring and their importance in the AI industry. Then we’ll…
