Key Insights

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  Evals consist of a task, a golden dataset with known correct outputs, and an evaluator that measures correctness.

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  Manual AI prompt testing is slow and inconsistent; automated evals accelerate and scale evaluation.

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  UX and product teams can and should learn evals as a practical, non-technical skill.

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  Creating your own golden dataset is essential and cannot be outsourced or fully automated.

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  Models are fixed once trained; improvements happen by refining prompts and context design, not retraining the model.

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  Evaluations measure task performance, not the underlying model itself, allowing comparison across models.

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  Outputting a confidence score from models is unreliable due to lack of internal memory and inconsistent scale interpretation.

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  Biases are baked into models during training via evals used in post-training refinement.

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  LLMs can be used to judge other LLM outputs to evaluate tasks with non-binary answers.

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  Effective eval work requires collaboration across data analysts, engineers, subject matter experts, and UX/product teams.

Notable Quotes