Key things to know about DeepSeek and evaluation

[jay_creates]

When working with DeepSeek, the biggest gains usually come from treating it as a repeatable workflow rather than a one-off prompt or experiment.

One important area is evaluation. 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 limitations. 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 DeepSeek becomes much more reliable when the workflow includes clear inputs, repeatable checks, and a way to learn from previous results.



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[clara2779]

On Key things to know about DeepSeek and evaluation, the part about deepseek and evaluation stands out to me. I would test one small workflow first, keep the variables consistent, and compare the result before changing several things at once. That way you can improve the workflow without losing track of what caused the improvement.

 

[zoespac]

For Key things to know about DeepSeek and evaluation, I would separate the problem into setup, output quality and repeatability. deepseek and evaluation fits into that because it can change the result quite a lot depending on the workflow. It is usually easier to troubleshoot when each change is tested on its own.