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Re: bad code Re: Subject: ChatGPT results are "subject

Subject: Re: bad code Re: Subject: ChatGPT results are "subject to review"
From: "John Lumley john.lumley@xxxxxxxxxxxxxx" <xsl-list-service@xxxxxxxxxxxxxxxxxxxxxx>
Date: Fri, 7 Jul 2023 13:39:43 -0000
Re:  bad code Re: Subject: ChatGPT results are "subject
John Lumley

> On 7 Jul 2023, at 14:35, Dave Pawson dave.pawson@xxxxxxxxx
<xsl-list-service@xxxxxxxxxxxxxxxxxxxxxx> wrote:
> o;?On Fri, 7 Jul 2023 at 14:26, Dorothy Hoskins dorothy.hoskins@xxxxxxxxx
> <xsl-list-service@xxxxxxxxxxxxxxxxxxxxxx> wrote:
> ...
> From what I see in the list of ChatGPT Code languages, it wasn't
> specifically trained on XSLT, so someone who builds their own training
> set will get better results. You folks probably have the best training
> examples in the world in the xsl-list.)
> Which begs the question, how might the xsl-list archives be ...
> declared / converted / made available (whatever) as training data?
>  And for this set (minor drawback), how to extract the 'eventual'
> solution from others proffered in error?
>> I don't think there's any going back, so the chances of people creating
code that won't run, that they can't debug themselves and which ChatGPT may
not provide the correction required if prompted, is high.
> And from your earlier comments, the more experience in writing xslt,
> the more likely you'll arrive at a solution using AI?
>> Michael, I wonder what "nasty accidents" you are thinking of -- some XSLTs
used in particular industries with real-world safety issues? Maybe we can
start to create some advice for clients on QA and testing protocols.
>  Is it logical to say that chatGPT will be just as easy to trip up as
> the man on the Clapham omnibus?
> regards
> ps. Tried google.bard with UK braille. Bit of a dogs breakfast.
> --
> Dave Pawson
> Docbook FAQ.

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