Changes between Version 15 and Version 16 of Public/WhitePaperMachineTalk

Timestamp:

May 4, 2023, 2:06:23 AM (20 months ago)

Author:

Boris Horner

Comment:

—

Public/WhitePaperMachineTalk

@@ -64 +64 @@
 
 == Summary
-Prompt Engineering and the skill to programmatically use AI to perform certain steps in a more complex process that contains other AI steps, steps done by humans and others performed by conventional software, are just emerging and are still lacking a clear definition. Temptation is probably high to write "Prompt Engineer" on ones business card after having played with ChatGPT for half an afternoon.
+Prompt Engineering and the skill to programmatically use AI to perform certain steps in a more complex process that contains other AI steps, steps done by humans and others performed by conventional software, are just emerging and are still lacking a clear definition. Temptation is probably high to write "Prompt Engineer" on one's business card after having played with ChatGPT for half an afternoon and seen its amazing capabilities.
 
-But while ChatGPT, due to its stunningly simple user interface, is accessible to young students who let AI write their homework, or practically to everyone, getting stable and high quality results from various sources without constant intervention is another level. Sometimes, minimal changes to the training data can make a rather big difference. Contradictions between rules and examples are a common mistake when editing and testing the training data, and they tend to bring the AI on thin ice. In some cases, the output can completely fail, because the AI is not prepared to something in the data, and it needs some testing to find out what it is and how to avoid it.
+But while ChatGPT, due to its stunningly simple user interface, is accessible to young students who let AI write their homework, or practically to everyone, getting stable and high quality results from various sources without constant user intervention is another level. Sometimes, minimal changes to the training data can make a rather big difference. Contradictions between rules and examples are a common mistake when editing and testing the training data, and they tend to bring the AI on thin ice. In some cases, the output can completely fail, because the AI is not prepared to something in the data, does not see how it could apply the rules, and it needs some testing to find out what it is and how to avoid it.
 
-I hope, this article gave you an impression that Prompt Engineering is more complex than typing something like "Hey, I got some text here, can you write it more clearly?" (even though this alone can yield surprisingly good results). Still, it's neither alchemy nor rocket science. It follows certain rules and best practices and can be learned.
+I hope, this article gave you an impression that Prompt Engineering is more complex than typing something like "Hey, I got some text here, can you write it more clearly?" (even though this alone can yield surprisingly good results). Still, it's neither alchemy nor rocket science. It follows certain rules and best practices and can be learned. And many people are just doing so in this very minute. I'm almost certain, within the next twelve months, I'll be surprised not too rarely about a new application no-one has ever expected.