Recent content by lewiswander

A useful way to understand Mistral AI is to separate the goal, the input, the model or tool, and the way the output is evaluated. One important area is limitations. Define what a good result looks like before changing tools or settings, then test one variable at a time so you can tell what...

The point about front-end and development 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 is usually easier to troubleshoot when each change is tested on its own.

A practical understanding of AI Level Generation starts with knowing what the system does well, where it is unreliable, and how to validate the result. One important area is customisation. Define what a good result looks like before changing tools or settings, then test one variable at a time...