Why building AI products is different

This is augmentation not automation. And that's kind of where we started with a lot of our thinking on this. And what made that unique is the way that we build is not through like one use case at a time. In some cases it's like a data set at a time of like this is what we need to be able to like understand. This is the problem space that we need to understand and there are different ways to approach it. You can fine tune, you can do something from retrieval, augmented generation and then you can do something just on general prompting. But each approach has pros and cons to it. So like being able to balance and and evaluate on an ongoing basis while knowing that your evaluation is never going to be comprehensive and there's going to be errors. And so having that full corpus of unknown means that we build from our principles and stick to our principles as we build versus having something linear that we know will work.