AI model performance and reliabliity There has been an explosion in the number of LLM models and apps over the past year. While it's important to move fast, it's equally important to think about the performance of these models and apps in the following ways. First, it's critical to know how your users interface with your model and how your model responds. Logging your production requests is the cornerstone of any LLMOps setup. Second, beyond measuring your usage, it's important to run stress tests to understand your model's weaknesses and get alerts if those failures come up in production. For example: if PII is leaked, or if the model starts giving irrelevant responses or hallucinating, you need to know ASAP. Finally, it's important to track your experiments and version your prompts, models, and datasets. At each iteration, you must run tests to guarantee you're always moving the needle forward, versus falling back. @openlayerco is a next-gen monitoring and eval platform that allows developers to keep track of how their AI is performing, beyond token cost and latency. Their comprehensive toolkit for AI reliability gives developers unique insights into the performance and reliability of their apps and models.