Free-tier eval advice: repeat a small task set rather than run a large one once

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The article argues that on a free allowance, the most useful spend is repeated runs of a fixed set of tasks rather than one pass over many tasks. Repeating a small pack several times exposes how stable a model's results are, whereas a single broad run gives a single number that can shift with minor prompt rewording. It stresses that variation between individual tasks usually matters more than variation between runs, so the tasks a product relies on should be repeated. The author recommends a frozen JSONL task pack with simple auditable graders, hashing prompts so mid-run edits remain visible, and logging tokens, latency, and failures for every call. The accompanying script is presented as a template to test on a couple of tasks before committing an allowance.

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