Ask HN: I learned useless skill of prompt engineering, how relevant will it be?

It depends on how you define, or what you include under, "prompt engineering". For some definitions it's not that valuable, but here's one definition that IMO is and will continue to be very valuable:

1. You have a lot of mileage with LLMs and AI systems in general (people who are exceptionally good at this have been reporting spending several hours daily working with AIs).

2. You already mastered a large number of useful tasks you can consistently and reliably complete using AI.

3. You continuously invent and discover novel ways to use AI and accomplish useful tasks.

4. You can use LLMs and other form of AI _programmatically_, by combining LLM calls as part of a larger and more complex process (ideally by writing code, though some people do that well using no-code tools or even just careful manual execution).

5. You can methodically examine and evaluate AI tasks, for example by developing evals and running them and analysing their results programmatically.

6. You keep up-to-date and consistently adapt to new developments, like new capabilities, models, libraries, etc ...

7. You can often come up with new ideas or translate existing requirements for tasks that can be achieved better or more efficiently (or achieved at all) using AI.

If the above is your definition of "prompt engineering" then yes, it's incredibly valuable, and even increase in value over time.

( x-posted on: https://everything.intellectronica.net/p/ad-hoc-definition-o... )

Neuro-linguistic programming (NLP) is a pseudoscientific approach to communication, personal development and psychotherapy, that first appeared in Richard Bandler and John Grinder's 1975 book The Structure of Magic I. NLP asserts that there is a connection between neurological processes (neuro-), language (linguistic) and acquired behavioral patterns (programming), and that these can be changed to achieve specific goals in life. [1]

Fascinating, people speculating about how people think and can be manipulated are applying those unporven highly suspectable "theories" to computers, which probably function totally different, but appear to show similarities.

[1] https://en.wikipedia.org/wiki/Neuro-linguistic_programming

edit: Oh, I just now saw "Output". So ChatGTP tries to steer you to become a NLP Practicioner :-)

Well I a right now descomposing business processes into atomic human actions (do X here, decide Y there, submit Z to there), get the interface into a usable form for scripting, and "engineer" a prompt to do exactly what a human does, which ultimately mostly is some kind of data classification or (very rare actually) transformation. Mostly its like: which of those things is important, how many of those are fake, or this thing contains the relevant bit of information I need to proceed.

So, its actually a lot of language tweaking to get just the right context/task description/data embeddings so the LLM (GPT3/4) gets it right >=90% of the time, which surprisingly often is better than actual humans, and in many cases there are also ways to detect imperfection and simply retry automatically which increases success chances even further.

The fetching/formatting/submitting data part (the manual coding) is getting easier over time, but the prompting remains somewhat, and I so far had no luck with any kind of recursion to let the LLM design its prompt, since ultimately all the specifics needed in the context has to somehow got into the context, which is me engineering it into big string structures.

probably doesn't sound shiny, but step-by-step making jobs irrelevant in businesses without sacrificing customizations. I think of it as a silent revolution thats happinging in many places now, ultimately making myself redundant, but hey the ride is fun!

They would probably not pass in the div or filler elements.

The reason I passed the div was I wanted things to be surrounded in that exact space. So when the model output it, the button would be in right place with right size.

The extra filler forms a guiding factor that helps will be stored in the context! I did that using GPT3.5.

You can now read new papers and follow ups from the community! It is really useful. You already know chain of thoughts. So you are on a good track!

Try to use these LLMs to automate more of the mundane tasks. Like scripting in bash, compiling videos, converting documents, refactoring data, transpiling code, transcribing code, etc. You will begin to see what works and what doesn't!

At the same time try to come with fun challenges for yourself to fool it. That will aid in learning ways to make it more obedient to you.