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It's okay; software engineering isn't real either. (In many countries, the word "engineer" is regulated -- you can't call yourself an engineer without professional qualifications and oversight.) https://en.m.wikipedia.org/wiki/Regulation_and_licensure_in_...
It's okay; software engineering isn't real either. (In many countries, the word "engineer" is regulated -- you can't call yourself an engineer without professional qualifications and oversight.) https://en.m.wikipedia.org/wiki/Regulation_and_licensure_in_...
It's okay; software engineering isn't real either. (In many countries, the word "engineer" is regulated -- you can't call yourself an engineer without professional qualifications and oversight.) https://en.m.wikipedia.org/wiki/Regulation_and_licensure_in_...
But prompt engineering? Based on what knowledge exactly?
Is this just about gatekeeping the word 'engineer', so that its purity isn't tarnished? I know that software engineers were scoffed at by electrical engineers, who I bet were scoffed at by mechanical engineers, who were probably scoffed at by, you know, people who built siege engines or whatever. Is prompt engineering 'real' engineering? It seems easy enough to test whether knowledgeable, self-proclaimed prompt engin…
But it certainly is based on knowing the inner workings of all or as many and as much of the abstraction layers.
But who's a prompt engineer? Is anyone who understands how Tensors and Transformers work is a prompt engineer? Are prompt engineers model specific? I'm GPT-3.5 prompt engineer and we have a job openings for Alpca or StableML prompt engineers?
Lastly, if these LLMs are so intelligent and have so much deeper understanding as their emergent capability - why prompt engineering is needed in the first place?
You generally don't need prompt engineering even with average IQ humans unless one is trying to swindle someone.
In other words, you also have to understand the text that you're getting back from your prompts, and then "engineer" the prompt to fish for better results. AI itself can do this to an extent, but over-engineering is also a thing, as is the fact that AI itself does not actually know what you want.
That's all there is to it. Refine, revise, engineer - all just buzzwords that lead to the same end result: a game of ping-pong between you and the LLM.
It's okay; software engineering isn't real either. (In many countries, the word "engineer" is regulated -- you can't call yourself an engineer without professional qualifications and oversight.) https://en.m.wikipedia.org/wiki/Regulation_and_licensure_in_...
That’s engineering.
What does matter is having stable, predictable abstractions that you can rely on, so in software engineering it would be a mistake to rely on system-dependent undefined behavior when using a language like C++. You also don't necessarily need to know what kind of low-level optimizations the compiler and linker are using to generate your executable binary, though in some cases it could be important.
With LLMs, however, it's not really clear if the same prompt always gives the same kind of output, and you can do 'regenerate response' with something like ChatGPT to see this in action.
In some sense, 'prompt engineering' can be more like 'interviewing an engineer' in that LLMs seem to provide better outputs if you start with a broad, general question and then narrow down to your specific interest over a series of questions. Helping the LLM out by defining context, asking it to expand on a specific output it generated, pointing out where it may be hallucinating etc. all appears to improve the quality of output but I don't know if this kind of iterative process is really 'prompt engineering', maybe 'prompt optimization' is a better word for it?
In other words, writing a series of prompts to an LLM that gets you the answer you need feels quite unlike writing a Python script to automate some task. You can plan the latter out from start to end, but the former is this back-and-forth process.
I'd say even in this example, there's more a sense of searching through a range of different options to achieve specific properties. This is the sense of "prompt engineering": you use skills to create prompts -- in part by searching through various alternatives -- that have specific properties.
[1] https://dictionary.cambridge.org/dictionary/english/engineer