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An AI asset can be a model configuration, a workflow, a prompt, or a maintained evaluation set. Its usefulness depends on the task and the conditions in which it runs. These guides focus on documented inputs, observable outputs, review effort, and versioned tests.
Begin with a small set of representative cases and a clear description of acceptable results. Compare candidates on those cases before adding complexity. Capture errors, retries, manual corrections, latency, and operating cost in the same record. A polished demonstration can be a useful starting point, but an evaluation needs ordinary cases, awkward edge cases, and a reproducible setup to support a working decision.
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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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