Key things to know about Large Language Models and testing

[amberthesouth]

A practical understanding of Large Language Models starts with knowing what the system does well, where it is unreliable, and how to validate the result.

One important area is testing. 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 accessibility. 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 Large Language Models becomes much more reliable when the workflow includes clear inputs, repeatable checks, and a way to learn from previous results.

 

[oscar1813]

I think large and language is the key part of this topic. A practical approach is to define the outcome you want, test it with a small example, and then check quality, speed and reliability separately. That should make it easier to tell whether the change is actually helping.

 

[erinworld56]

I think large and language is the key part of this topic. A practical approach is to define the outcome you want, test it with a small example, and then check quality, speed and reliability separately. That way you can improve the workflow without losing track of what caused the improvement.