Databricks Certified Generative AI Engineer Associate Study Guide

Chapter 8. Monitoring and Evaluating LLMs in Production Environments

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Large language models (LLMs) deliver significant value when deployed in production. But their real impact depends on how effectively you track their behavior, evaluate their outputs, and ensure they operate reliably under real workloads. In this chapter, you will explore the monitoring and evaluation practices that determine whether a deployed LLM continues to meet quality, safety, and cost expectations. Production systems require ongoing oversight, which becomes especially important when the model interacts with critical business processes or customer-facing applications.

Organizations increasingly rely on model outputs to make decisions, automate workflows, and augment human work. As a result, operational failures such as unexpected drift, incorrect outputs, increased latency, or cost overruns can have broad consequences. This chapter explains how monitoring frameworks, evaluation pipelines, and Databricks tools help you ...