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You will be surprised how often ChatGPT will correctly explain why or how the answer it has given you is utter nonsense. It is fascinating in a way.
[1] https://civitai.com/models/28059/icbinp-i-cant-believe-its-n...
What matters is whether the suggestions are actually good, not where they came from.
It's messing up the dynamic where creatives come up with blue sky stuff and developers come to a compromise on a possible solution. Now you have this AI model hallucinating plausible, but fake solutions.
The model says what they want to hear because it is a chicken, not a pig in this scenario.
You can create counterpoint arguments with ChatGPT... Sh t in / Sh t out. Everybody happy. Years back during big outsourcing project, this never ending conversation (between human teams) were big part of it.
I'd say tell them to ask GPT why it won't work. It'll say something like "You're right, I've made a mistake...". This should illustrate that it can be helpful, but it's just aiming to please whether or not the solution is workable or sane.
I have found that you can reduce that behavior if you prompt it correctly, though.
What matters is whether the suggestions are actually good, not where they came from.
The author makes it clear in the associated discussion that he doesn't immediately assume the suggestions are bad based on their source: > I take the time to understand and consider each suggestion, not rejecting anything out of hand, and share them with my team members (of which there are 17). The issue is the time it takes to explain _why_ these are bad ideas to non-technical (and skeptical) colleagues.
You can create counterpoint arguments with ChatGPT... Sh t in / Sh t out. Everybody happy. Years back during big outsourcing project, this never ending conversation (between human teams) were big part of it.
I'd say tell them to ask GPT why it won't work. It'll say something like "You're right, I've made a mistake...". This should illustrate that it can be helpful, but it's just aiming to please whether or not the solution is workable or sane.
> User: how can I pass two parameters to a hugo partial as positional parameters (without using a dict)?
> ChatGPT: If you want to pass parameters as positional arguments without using a dictionary, you might need to work around this limitation. One approach is to use a custom data structure, such as a slice or an array, to emulate positional parameters. Here's an example:
{{/* In your main template */}}
{{ partial "yourPartial.html" "value1" "value2" }}
{{/* In yourPartial.html */}}
{{ $param1 := index . 0 }}
{{ $param2 := index . 1 }}
> User: Does that work?
> ChatGPT: I apologize for the confusion in my previous response. I made an error in the example provided. Hugo's templating system doesn't support passing parameters as positional arguments directly, and my previous example was incorrect.
Earlier quoted context omitted.
The issue here is that the artists are confident that the suggestions (which they don't themselves understand) are good because they came from ChatGPT.
I would argue that if you cannot answer questions on something generated before suggesting it or handing it off, you absolutely should not be pushing for it. Do the research necessary to feel fully confident on a hand-off.
GPT can be a fantastic time saver, but it is unable to check itself.
You absolutely need to do the research to check it and correct its output.
It's like a power tool: potentially very powerful, but it'll make a huge mess if you let it run out of control.
What a shitty place that website has become, dang!
Tell your coworkers to ask ChatGPT "why does this solution doesn't work? and what is wrong with it?" You will be surprised how often ChatGPT will correctly explain why or how the answer it has given you is utter nonsense. It is fascinating in a way.