Using LLMs as judges for evaluating outputs is promising, but it requires careful prompt engineering to ensure accurate grading of responses.

There are three types of evaluations: offline evals, online evals, and in-the-loop evals, each serving different purposes in the evaluation lifecycle.

Evaluations consist of two parts: data and evaluators, which can vary depending on whether you're using ground truth or reference-free methods.

We're launching open-source evaluators that simplify the process of evaluating code, retrieval-augmented generation, extraction, and tool calling, making it easier for developers to get started.

In the context of AI evaluation, benchmarks are like supervised learning, while LMArena operates more like reinforcement learning, allowing for more dynamic and real-world testing.

The unique aspect of LMArena is its ability to evolve over time, adapting to the changing landscape of AI evaluation.

Expert evaluations are valuable, but they must be complemented by broader community input to avoid bias in AI assessments.

Expert evaluations are valuable, but they must be complemented by broader community input to avoid bias in AI assessments.

Using large language models as judges, combined with human review, allows for scaling without overwhelming human subject matter experts.