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Evaluation turns a broad claim about quality into a task, a set of inputs, and an observable standard for success. For AI and prompt workflows, it also requires attention to setup, output variability, review effort, and failures that a polished example may omit.
Separate development cases from the cases used to compare candidates. Keep the same scoring instructions, record the version of each component, and preserve representative failures. The guide below explains a practical workflow for reviewing useful performance in context, including when a human needs to examine the result and when the evidence is too limited to support a conclusion.
1 guide about evaluation
A persuasive demo is the beginning of an AI review. Define the task, build representative tests, compare outputs without brand labels, track failures and costs, and preserve enough detail to understand what changed between evaluations.
Read the guide: A Practical Evaluation Workflow for AI, LLMs, and PromptsQuestions worth asking
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