That feeling of not knowing what to do with it is an example of humans being stupid. We are all victims of being "Johnny" in this paper: https://dl.acm.org/doi/full/10.1145/3544548.3581388
Why Johnny Can’t Prompt: How Non-AI Experts Try (and Fail) to Design LLM Prompts
J.D. Zamfirescu-Pereira, Richmond Y. Wong, Bjoern Hartmann, Qian Yang
2023 · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
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I love this whole series on misconceptions! Our expectations here are very much set by human-human interactions: we expect memory, introspection, that saying approximately-the-same-thing will give us approximately-the-same-result, that instructions are better than examples, that politeness helps, and many more [1] -- and some of these expectations are so deeply rooted that even when we know, intellectually, that our expectations are off, it can be hard to modify our behavior. That said, it will be super interesting to see how people's expectations shift -- and how we bring new expectations from human-AI interactions back to human-human interactions. [1]: https://dl.acm.org/doi/pdf/10.1145/3544548.3581388 (open access link)
For raw text completion I agree with you that it's a bit discordant. IMO text completion prompts work better when you use more of a first-person, here-is-the-beginning-of-some-transcript style. The OpenAI chat completion endpoint encourages the second-person prompting you describe, so that could be why you see it a lot. My understanding is that a transformation is applied to the user input prompts before being fed to the underlying model, so it's possible that the model receives a more natural transcription-style prompt. You might be interested in this paper, which explores ways to help non-experts write prompts https://dl.acm.org/doi/abs/10.1145/3544548.3581388.
I'm super glad that Anthropic has put this up -- having a set of examples is super critical for folks who are writing prompts. (In fact my collaborators and I wrote a paper about these challenges last year [1].) But I also wish they had included more than one example input/output pair for each prompt type: people really pattern match hard on single examples for prompts, and don't get a good sense for what's critical and what's optional in any given prompt or input from just one example. It wouldn't be that much work to have a carousel of input/output pairs here that readers could flip through, and I suspect it would make this page 10X more useful. If anyone from Anthropic…
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is reading here, I can't emphasize this enough! It's not just about "well, users should experiment" because often users don't know what they can effectively experiment with in these prompts, because it's hard to extrapolate from just one working example. Multiple examples (even just 2-3!) will give readers much more background here! [1] https://dl.acm.org/doi/pdf/10.1145/3544548.3581388 (open access link)
If you are someone who was working in LLMs/Generative AI before it got "cool", The market has fundamentally changed and changed extremely in your favor. Talent is extremely scarce and expensive right now in this space. Ask for well above FAANG compensation. Try to shop around for offers. I had 4 offers and a bidding war for my talent (with no leetcode - but I have an extensive github and publications about LLMs), when previously these same companies would have brutally leetcoded me. I thought that there would be a ton of people getting really good really fast with Generative AI. It turns out that most people are terrible at using the tools, and there's even research about this right now - https://dl.acm.org/doi/10.1145/3544548.3581388
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