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A useful way to understand Datasets is to separate the goal, the input, the model or tool, and the way the output is evaluated. One important area is quality versus speed. Define what a good result looks like before changing tools or settings, then test one variable at a time so you can tell...

When working with Claude, the biggest gains usually come from treating it as a repeatable workflow rather than a one-off prompt or experiment. One important area is performance. Define what a good result looks like before changing tools or settings, then test one variable at a time so you can...