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A useful way to understand Large Language Models is to separate the goal, the input, the model or tool, and the way the output is evaluated. 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...

I wanted to start a broader ideas worth trying thread around Ideogram. A useful angle is collaboration, but it also connects with evaluation. Different tools and workflows can produce very different results even when the goal is similar. It would be interesting to compare practical examples...