I am not dismissing this as being slop and actually have no beef with using LLMs to write but yes, as you call out, I think it's just poorly written or perhaps I'm not the specific audience for this.

Sorry if this is bad energy, I appreciate the write up regardless.

Where it gets interesting is being pushed into directions that you wouldn't have considered anyway rather than expediting the work you would have already done.

I can't speak for engineers, but that's how we've been positioning it in our org. It's worth noting that we're finding GenAI less practical in design-land for pushing code or prototyping, but insanely helpful helping with research and discovery work.

We've been experimenting with more esoteric prompts to really challenge the models and ourselves.

Here's a tangible example: Imagine you have an enormous dataset of user-research, both qual and quant, and you have a few ideas of how to synthesize the overall narrative, but are still hitting a wall.

You can use a prompt like this to really get the team thinking:

"What empty spaces or absences are crucial here? Amplify these voids until they become the primary focus, not the surrounding substance. Describe how centering nothingness might transform your understanding of everything else. What does the emptiness tell you?"

or

"Buildings reveal their true nature when sliced open. That perfect line that exposes all layers at once - from foundation to roof, from public to private, from structure to skin.

What stories hide between your floors? Cut through your challenge vertically, ruthlessly. Watch how each layer speaks to the others. Notice the hidden chambers, the unexpected connections, the places where different systems touch.

What would a clean slice through your problem expose?"

LLM's have completely changed our approach to research and, I would argue, reinvigorated an alternate craftsmanship to the ways in which we study our products and learn from our users.

Of course the onus is on us to pick apart the responses for any interesting directions that are contextually relevant to the problem we're attempting to solve, but we are still in control of the work.

Happy to write more about this if folks are interested.