Re:Consensus: Use AI to find insights in research papers
by xigency on Thursday August 17, 2023 @06:29PM
(#37169075)
Effective prompting has more to do with theater than engineering.
Re:Consensus: Use AI to find insights in research papers
by bee_rider on Thursday August 17, 2023 @11:14PM
(#37171055)
Yes, and-gineering.
Re:Consensus: Use AI to find insights in research papers
by xigency on Friday August 18, 2023 @08:49PM
(#37184205)
Clever.
Re:Consensus: Use AI to find insights in research papers
by reissbaker on Thursday August 17, 2023 @08:07PM
(#37169972)
IMO you're right that "prompt engineering" is a cringe-y term because it implies what you're doing is mostly writing prompts. That being said, I don't think that's what it actually entails, any more than "backend engineering" is mostly writing SQL queries. Prompt engineering is building the systems around the prompts e.g. writing the LangChain or whatever code, parsing stuff and interacting with structured DBs, message queues, etc (and occasionally writing prompts too, but that's a relatively smaller part). It requires some domain-specific knowledge e.g. chain of thought and retrieval augmented generation techniques, some basic linear algebra, keeping up to date with new models and new ways of running them (ggml? gptq? openai functions vs logit masking llama 2?), but it's more or less backend engineering with a twist.
I've seen some of the more serious people switch to the term "Applied AI" which I think encompasses the role a lot better. Also I've seen a decent number of grifter types saying they're "prompt engineers" when what they mean is they're writing prompts into ChatGPT's UI, which I think is part of what drives the cringing feeling when you hear the phrase "prompt engineer" and probably drives some of the movement away from the term for engineers.
Re:Consensus: Use AI to find insights in research papers
by jimmySixDOF on Friday August 18, 2023 @04:51AM
(#37172940)
This is a great recap of the present role. I have been following a conversation around the AI Engineer as a label (although I like Applied AI better) from the Latent Space podcast team so a fair amount of meme hypecycle, but also active and actionable. They are setting up a conference in October and you can see the discussion unfold around the blog post below on X-itter.
Comparing to google search, there definitely is skill involved in knowing how to google things well. We're all accustomed to googling things many times per day so I think a lot of people forget that being able to google things and get the results you want is a skill that has to be learned.
But I would never refer to being "good at writing google search queries" as any kind of engineering. But is becoming good at searching google any less difficult than getting good at writing LLM prompts?
I'd love to hear the other side of the argument. How difficult is it to become good at "prompt engineering"? Why do we even call it "prompt engineering" instead of just "writing effective prompts"?
Edit: I think the main gripe I have with the term "prompt engineering" is it makes the skill of writing prompts sound a lot harder than it actually is. Maybe I'm underestimating how difficult it is to learn how to write good prompts?