There are three types of evaluators: deterministic code-based evaluations, LLM-as-judge techniques for more complex assessments, and human annotation for real-time feedback.

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

We have developed an internal evaluation system to measure our models, as relying on external benchmarks often leads to data contamination and unreliable results.

The evaluation process is continuous; it's not enough to just build a dataset and run it once. Ongoing evaluations are necessary to maintain quality in production systems.

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.

Creating high-quality evaluation datasets is essential for developing effective control measures, as benchmarks help optimize performance.

Building effective control evaluations is difficult, as they need to closely resemble actual deployment settings to yield valid insights.

It's crucial for application developers to build their own datasets and evaluators, as academic datasets often do not represent real user interactions. To build effective evaluators, it's essential to create datasets tailored to specific use cases, which can be easily done in LangSmith by adding input-output pairs to a dataset.