Recent content by skyben

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

I think courses and certifications 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. It also gives you a cleaner baseline if you want to compare tools or settings later.

For Local LLMs, it helps to think in terms of inputs, constraints, verification, and iteration. One important area is reliability. 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...