Writing effective evals is becoming a critical skill for product managers. Evals are like quizzes for models to gauge how well they know a certain set of subject material or how good they are at responding to specific questions.

Online evals assess the app's performance in real-time using actual production data, allowing for immediate tracking of performance.

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

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

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

It's important to evaluate prompts by testing them in real scenarios, as this can reveal flaws in the prompt design that need addressing.

It's essential to understand that while the product is crucial, leveraging data effectively can provide significant business advantages.

The most valuable asset for AI companies is not just their codebase but the meticulously labeled evaluation sets that define correct outputs for AI models.

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.