Using Custom LLM Evaluations to Build Reliable AI Applications
If you develop applications powered by large language models (LLMs), the ability to create custom LLM evaluations is instrumental for understanding how your application will behave in the hands of users. LLM-powered apps typically directly display the LLM's output to your end users, so if the LLM produces incorrect responses, your user experience suffers — or worse. By evaluating the output of the LLM against custom-created rules and expectations, you can reduce the likelihood of your LLM app behaving in ways that you or your users didn’t expect.
While the AI industry uses standard metrics to evaluate LLMs, the way that your app uses an LLM is unique to your specific use case, therefore the industry metrics don’t necessarily apply or don’t tell the full story. Rather than relying on the standard LLM metrics, what you need is a set of custom LLM evaluations that are aligned with your use case and customer expectations.
In this guide, we explain what custom LLM evaluations are and how you can use Okareo to customize your LLM evaluation. We’ll also show you how you can automate the custom evaluation to be run whenever you make a change in your LLM usage inside your app.
