Local AI Help knowledge guide: improving privacy considerations

[callumsign]

A useful way to understand Local AI Help is to separate the goal, the input, the model or tool, and the way the output is evaluated.

One important area is privacy considerations. 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 future improvements. 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 Local AI Help becomes much more reliable when the workflow includes clear inputs, repeatable checks, and a way to learn from previous results.

 

[waves_oscar]

The point about local and help is interesting. In practice I would keep the first test simple, record what worked, and only add extra complexity if it solves a specific limitation. It also gives you a cleaner baseline if you want to compare tools or settings later.

 

[arialogi]

One thing worth trying with Local AI Help knowledge guide: improving privacy considerations is a controlled comparison around local and help. Use the same input for each test and note what changes in the output rather than judging from a single run. That should make it easier to tell whether the change is actually helping.