The article discusses seven techniques for evaluating and monitoring the performance of large language models (LLMs). These techniques include LLM-as-a-Judge, ML-model-as-Judge, Embedding-as-a-source, NLP metrics, Pattern recognition, End-user in-the-loop, and Human-as-a-Judge. Each technique has its pros and cons, and the choice of which one to use depends on factors such as cost, latency, setup, explainability, etc. The article also provides a comparison chart for these techniques and offers insights into how they can be used in combination to provide a more comprehensive understanding of LLM performance.