We actually experienced this first-hand when building qURL and launching with our initial users as well, it was quite interesting.
We knew that what we're building is high impact for AI/agentic world due to the nature of expiring gated-access links for sharing local resources, which is not really something that our competitors (ngrok or cloudflare or tailscale) could offer as well as we could.
People were pretty excited, but when we were getting them to try out qurl and running locally, they'd run into all sorts of issue where their coding agent would get confused or distracted and wouldn't actually do the setup we needed them to do (just a few `qurl <param>` commands). It was weird because our docs were public, and github was fully searchable, so we kept wondering why the agent wouldn't just search for it and figure it out?
What we realized was that our target audience here, which were largely not-super-technical people who more so cared about vibe coding demos rather than doing proper software engineering (as that wasn't their job), didn't really prompt in the same way as software people like us did. They didn't really "know" how to talk to AI.
In particular: they prompted with one-shots and incomplete/unspecific requests, asking for things like `share my app with qurl`; the agent would intuitively know "I have to search for qurl and figure out how to use it", so it would do that; but then, it would ask random other things (to confirm) about homebrew, local setup, preferences, etc. - and since these questions were often roughly technical, many of our users would get derailed and more confused. So, they'd start a conversation with the agent instead of asking it directly, leading to both of them being confused... which would eventually lead to the agent just suggesting something like ngrok or cloudflare to unblock the user's line of questioning.
It was weird - but we tested this internally and reviewed the traces, and found that it just came down to prompting habits ultimately: AI is trying to both give you as much info as it can, be as succinct as possible, and also not assume too much about your request all at the same time. It's a REALLLY hard balance to strike. (so it makes sense that it's not perfect)
So as a result, when non-technical people were asking to deploy a qurl, the moment the agent would get stuck and need their help, they naturally wouldn't know how to answer or what it was even asking, so the simple request would spiral into its own conversation fork and the `deploy with qurl` point would get lost in context. The prompting advice we came up with for them was this: "if your agent gets stuck, just tell it to search online and figure out how to unblock itself." Because of the balance it tries to strike, it sometimes just needs that little approval to just go and look things up on the internet, even if it is wasting tokens doing so.
So the point here isn't so much that it takes skill, but rather it just takes intuition about what AI is good at/bad at and what sort of personality it's being trained to embody. Once you get the hang of that, even if you know nothing about coding at all, you can become pretty effective with your AI toolset simply by virtue of knowing what directions to guide it to and watch pitfalls out for/how to unblock it when needed.
Our 2c on the topic!
We knew that what we're building is high impact for AI/agentic world due to the nature of expiring gated-access links for sharing local resources, which is not really something that our competitors (ngrok or cloudflare or tailscale) could offer as well as we could.
People were pretty excited, but when we were getting them to try out qurl and running locally, they'd run into all sorts of issue where their coding agent would get confused or distracted and wouldn't actually do the setup we needed them to do (just a few `qurl <param>` commands). It was weird because our docs were public, and github was fully searchable, so we kept wondering why the agent wouldn't just search for it and figure it out?
What we realized was that our target audience here, which were largely not-super-technical people who more so cared about vibe coding demos rather than doing proper software engineering (as that wasn't their job), didn't really prompt in the same way as software people like us did. They didn't really "know" how to talk to AI.
In particular: they prompted with one-shots and incomplete/unspecific requests, asking for things like `share my app with qurl`; the agent would intuitively know "I have to search for qurl and figure out how to use it", so it would do that; but then, it would ask random other things (to confirm) about homebrew, local setup, preferences, etc. - and since these questions were often roughly technical, many of our users would get derailed and more confused. So, they'd start a conversation with the agent instead of asking it directly, leading to both of them being confused... which would eventually lead to the agent just suggesting something like ngrok or cloudflare to unblock the user's line of questioning.
It was weird - but we tested this internally and reviewed the traces, and found that it just came down to prompting habits ultimately: AI is trying to both give you as much info as it can, be as succinct as possible, and also not assume too much about your request all at the same time. It's a REALLLY hard balance to strike. (so it makes sense that it's not perfect)
So as a result, when non-technical people were asking to deploy a qurl, the moment the agent would get stuck and need their help, they naturally wouldn't know how to answer or what it was even asking, so the simple request would spiral into its own conversation fork and the `deploy with qurl` point would get lost in context. The prompting advice we came up with for them was this: "if your agent gets stuck, just tell it to search online and figure out how to unblock itself." Because of the balance it tries to strike, it sometimes just needs that little approval to just go and look things up on the internet, even if it is wasting tokens doing so.
So the point here isn't so much that it takes skill, but rather it just takes intuition about what AI is good at/bad at and what sort of personality it's being trained to embody. Once you get the hang of that, even if you know nothing about coding at all, you can become pretty effective with your AI toolset simply by virtue of knowing what directions to guide it to and watch pitfalls out for/how to unblock it when needed.
Our 2c on the topic!