When working with Neural Networks, the biggest gains usually come from treating it as a repeatable workflow rather than a one-off prompt or experiment.
One important area is useful tools. Define what a good result looks like before changing tools or settings, then test one variable at a time so you can tell what actually improved the outcome.
Another area is reliability. Keep examples of both strong and weak outputs, check important facts or generated code independently, and document settings that produce consistent results.
The main takeaway is that Neural Networks becomes much more reliable when the workflow includes clear inputs, repeatable checks, and a way to learn from previous results.
One important area is useful tools. Define what a good result looks like before changing tools or settings, then test one variable at a time so you can tell what actually improved the outcome.
Another area is reliability. Keep examples of both strong and weak outputs, check important facts or generated code independently, and document settings that produce consistent results.
The main takeaway is that Neural Networks becomes much more reliable when the workflow includes clear inputs, repeatable checks, and a way to learn from previous results.
