If you ask an agent to “spin up a SaaS product” it’ll sprinkle in some sampling but it’s mostly going to do the task based on the mean of its training data. I find it helpful to think of prompting as pulling the agent directionally away from the mean.
Models are great at CRUD, and they do that well because CRUD is the mean. If you want models to be fantastic, picking Python’s a good choice, because they’ve seen so much Python. But, what if we don’t want the mean? What if we want something different? The further you need to go, the more effort it takes. The harder the model has been trained on…
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