How do you evaluate the accuracy and reliability of AI models in production?

Once an AI model is being used in a real application, how do developers make sure it continues to give accurate and reliable answers?

Honestly I love the Weights and Biases platform. I’ve been using their post training SDK for awhile now and I can barely prompt an LLM without it hooked up and in the loop-anymore. It’s no additional cost if you have a WandB account too which is a bonus. The model tracing is extremely detailed. Just for starters it has inputs, outputs, latency, token count, cost, nested call tree. And you can connect it to evaluations and set an LLM as judge to monitor as frequently as you prefer. Just those 2 tools are enough to make me feel at ease and I’m barely scratching the surface o all that they have to offer.

Weave