Active Prompting: A Comprehensive Guide for the Future of Prompt Engineering(thepromptengineer.org)
thepromptengineer.org
Active Prompting: A Comprehensive Guide for the Future of Prompt Engineering
https://www.thepromptengineer.org/exploring-active-prompting-a-comprehensive-guide-for-the-future-of-prompt-engineering/
4 comments
This post is AI-generated spam. There’s whole paragraphs that lose track of the fact this is about LLM prompting and talk about prompting humans with body language.
It sounds like it was generated by AI, and that made it really boring to read through
Funny, but no. You seem to have a limited view in what prompt engineering is and the history of prompting. Prompting in software engineering is more than just using LLMs. Before LLMs, there were static choices with limited variations of inputs like the old chat based applications, auto response options on phone menus, and other such applications. Today, in addition to the ChatGPT like applications reliant on LLMs, there are other ways of prompting a human for an input to use in an application. One such example is with physical gestures in virtual reality.
Here's the relevant section from what I can gather from parent's response:
> These are especially applicable in human-computer interaction interfaces where physical movements act as prompts.
Here's the relevant section from what I can gather from parent's response:
> These are especially applicable in human-computer interaction interfaces where physical movements act as prompts.
Speaking as someone not really involved in LLMs: i get the strong impression that prompt engineering is a scam (in the classical sense: taking advantage of an ill-understood new field to milk clients for cash).
It’s difficult to believe that in a system that’s (a) non-deterministic, and (b) is prone to sometimes serious hallucinations, prompt engineering would be generally effective.
Are there any good counter-arguments to this?
It’s difficult to believe that in a system that’s (a) non-deterministic, and (b) is prone to sometimes serious hallucinations, prompt engineering would be generally effective.
Are there any good counter-arguments to this?
My novice understanding of prompt-engineering is that it "guides" the LLM to certain parts of the output space. It has all this information related to a topic from a variety of sources, by creating some context (i.e. prompting) one can increase the likelihood of the LLM outputting text that's more related to that part of the training data that you'd want.
For example, let's say the LLM learnt about color theory from: 1. academic articles, 2. artistic discussion forums, 3. reddit ( :) ).
If you wanted to steer the output from a certain "niche" you could create context in the prompt that makes the LLM more likely to create output (ie a continuation of the prompt, sort of). Tricks I've seen is stuff like "You're a super-smart, best in your field XYZ, tell me something about...". But I bet you could similar good output by using lingo and vocab specific to either niche to "prime" the LLM.
Take with lots of salt-grains, just my pet-theory.
For example, let's say the LLM learnt about color theory from: 1. academic articles, 2. artistic discussion forums, 3. reddit ( :) ).
If you wanted to steer the output from a certain "niche" you could create context in the prompt that makes the LLM more likely to create output (ie a continuation of the prompt, sort of). Tricks I've seen is stuff like "You're a super-smart, best in your field XYZ, tell me something about...". But I bet you could similar good output by using lingo and vocab specific to either niche to "prime" the LLM.
Take with lots of salt-grains, just my pet-theory.
It's as much a scam as SEO used to be, or still is. Yes there are some techniques, that help you get better results, like providing examples and being precise. Especially local LLMs tend to have their own litte quirks, which you'll have to work around.
Same with SEO, yes, putting all you content in an image is bad, but as a rule of thumb ranking = relevancy * content * honest backlinks * google secret sauce.
If you are able to avoid the obvoius footguns, there is no need to ever hire a Prompt-Engineer /SEO-"expert"
Same with SEO, yes, putting all you content in an image is bad, but as a rule of thumb ranking = relevancy * content * honest backlinks * google secret sauce.
If you are able to avoid the obvoius footguns, there is no need to ever hire a Prompt-Engineer /SEO-"expert"
While they’re non-deterministic at a token level, they’re also still pattern following systems.
Prompt Engineering is about (a) getting more deterministic outputs from the model, (b) using the models’ own bias to get more useful output, (c) refining the prompt to shorten it and reduce costs at scale, and (d) using them as building blocks for higher-level functionality.
As a disciple (if it can even be called that) it may very well disappear as the tech progresses.
Edit to also say: you are correct that there are a lot of people selling secret sauce or magic techniques and those are pretty scammy.
Edit to also say: you are correct that there are a lot of people selling secret sauce or magic techniques and those are pretty scammy.
First, you're right in that articles like this are spam for click-throughs.
Writing prompts is like writing good search queries, and the techniques developed today will fall away tomorrow as the models and generation parameter settings get better. This has happened rapidly with diffusers, and will likely happen with language models.
The system isn't non-deterministic, although there are a fair number of parameters used to try to make it seem like it (temperature in particular). But given the same prompt, zero temperature, and the same seed, you'd get the exact same output each time. The injected pseudo-randomness is deliberate.
Having said all that, there are a handfuls of key phrases or instructions that do allow you to get more than you would from simple conversation English. For example, asking a model to use Tree of Thoughts and Chain of Thoughts in calculating its answers does have an effect. The same with asking "are you sure" for some models can yield corrections and refinements.
Writing prompts is like writing good search queries, and the techniques developed today will fall away tomorrow as the models and generation parameter settings get better. This has happened rapidly with diffusers, and will likely happen with language models.
The system isn't non-deterministic, although there are a fair number of parameters used to try to make it seem like it (temperature in particular). But given the same prompt, zero temperature, and the same seed, you'd get the exact same output each time. The injected pseudo-randomness is deliberate.
Having said all that, there are a handfuls of key phrases or instructions that do allow you to get more than you would from simple conversation English. For example, asking a model to use Tree of Thoughts and Chain of Thoughts in calculating its answers does have an effect. The same with asking "are you sure" for some models can yield corrections and refinements.
Prompt engineering is the new seo
It's a great title but the content doesn't live up to it. I recommend ignoring the upvotes on this one and not reading the article. In reality it's not really even a guide of anything, is disjointed, and can be completely summarized in 2 sentences as "AI doesn't currently use use nonverbal cues. Also, make your prompts concise." I'm halfway sure the entire thing is AI-generated.
And lest you think this criticism is unfair, take a look at the author's 24 hour submission history-- 3x for this website in the past 15 hours.
And lest you think this criticism is unfair, take a look at the author's 24 hour submission history-- 3x for this website in the past 15 hours.