Comment by cloakandswagger
4 days ago
Remember in 2023 when people thought "prompt engineering" would be the new software engineering and invested tons of time into learning CoT, ReAct, thread-of-thoughts, etc?
Those were mostly obviated by reasoning models and harness updates by 2024.
It seems pointless to invest energy into the latest/greatest AI technique or framework when they're going to either be absorbed or replaced on a 3 month cycle.
Isn't it clear that some people are better at working with/prompting LLMs than other people? Or is the idea that what you write to them and how you use them doesn't matter, it's all up to the model/harness? To me this seems clear, so then clearly this is a skill, which typically is called "prompt engineering". Specifically CoT or the other things you mention wasn't referred to as "prompt engineering" as far as I know, that skill is more about how you communicate with the LLMs and how you use them, rather than what specific processes/workflows/technologies you use.
I actually think that good prompting MOSTLY comes from good writing skills in general. Being able to more clearly state things to an agent, knowing what pieces of context are entirely unnecessary and which are important, having a larger vocabulary helps too.
Of course, there are other areas that can improve model output (Direction rather than open-ended assistance requests, using keywords + plugins that help, the "your output should include: " style prompting).
A few of us run almost the same exact setup at my shop (Base Claude Code w/ SuperPowers + a context repository) and the models are somewhat unhelpful to some, and give meaningful output to others. The only correlation I notice is that their prompts are no-good. Not from a meta "prompt" engineering standpoint, but from a general English 101 standpoint.
"dudde no i wanted the function to return 3 things. not like that. do it again"
VS something like
"Modify the "renderThreeVars()" function signature to accept another variable called "z" and add it to the return statement at line 64."
Obvious exaggeration, but you get the point.
Why not open-ended assistance requests?
I ask it all the time about whether X is feasible, how we can get started on Y, and to investigate issue Z.
It is working great for me in a >100k LOC project.
Perhaps this works less well with weaker models. I suspect the people who say Qwen 3.6 27B is working well, are using prompts like "modify the renderThreeVars() function in rendering.py".
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Is Superpowers any good? My coworkers who've used it seem to think that its main purpose is to consume a lot of tokens.
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> Obvious exaggeration, but you get the point.
From some very quick tests, I get the impression that having good grammar, punctuation etc. is really not important (although I try to do it anyway because I'm accustomed to trying to do it), but clarity and precision definitely are. And of course, if you have unknown unknowns, they do need to get figured out before progress is possible. But with the right mindset, that just means you need an extra turn or two, not that you're going to end up at a dead end. (... I guess that counts as a pun?)
Well said. I’ve been trying to put my finger on this for a while. The interaction plane for an LLM is __all of human language__
Speaking a prompt with a long ramble is very powerful.
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As part of a previous job I needed to audit internal AI usage from a largely non-technical employee population.
The prompts were, predictably, really bad. Broken English, sentence fragments, vague requests, lack of context. Yet somehow, the users always got the answer they were looking for. It might have taken a few extra turns with questions from the model, but the end result was the same.
It's humbling, but a flowery, carefully crafted prompt is at best slightly more efficient than a "CAN A DOG BE EATIN SUN FLOWER SEED?" peasant prompt.
You call that a peasant prompt, but it's actually almost perfect. Couple notes, but it's 95% of the way there. "Can a dog eat sunflower seed" is probably the perfect version, just 1 extraneous word in this version.
Unless the user wanted to know if a cat could eat sunflower seed or something.
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"dog sunflower seeds" is all the context an LLM needs, any other words are extraneous.
They don't need to be told that you're asking about the safety of eating them, because they can infer that based on the fact that a very large percentage of any text linking dogs to sunflower seeds is obviously going to be about the safety of the dog eating them.
Even pre-LLM that would have been a perfectly sufficient google search for the same information.
> Broken English, sentence fragments,
Why is would that be "bad prompt"? It is machine inpit, if machine can interpret fragment all the better.
Nah, you might be confusing prompt engineering with having domain knowledge. :)
I think some people who are better at "prompting" even without domain knowledge could be better at getting LLM agents to produce good results than people with good domain knowledge but without the skills to prompt well. Just a hypothesis though, would be fun to try it out for real sometime :)
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What's clear is that there is a lot of hype around LLM and people who were previously valued for their IC are now in the business of shilling.
RL has basically killed prompt engineering. You still need to provide the right context and process, but how you communicate with them beyond that is no longer so important.
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> Isn't it clear that some people are better at working with/prompting LLMs than other people?
Sure, but I think it's essentially just that people who are better at traditional non-agentic software engineering are better at agentic software engineering. The only exception would be individuals who avoid agentic coding due to skepticism, hostility, or lack of opportunity.
I feel the same way about "prompt engineering" as I feel regarding the term "parkour" - you know, running and jumping on stuff.
Are people really putting on their resumes that they are capable of reading and writing and appropriately defining and limiting context? That's all prompt engineering is - it's being able to communicate effectively and elucidate your objectives.
Congratulations to all you English majors out there, you're about to make $350K/year.
Personally I don't, but why not? People aren't embarrassed to put their language skills ("be able to communicate in this specific language" - not special, it's just another language?), their leadership skills ("effective business communication" - big deal) or that they are a people-person ("can talk with others" - most people can do this) on their resume.
> Are people really putting on their resumes that they are capable of reading and writing and appropriately defining and limiting context?
People put whatever buzzwords will get them through initial screening. My resume contains tons of banal shit like agile, automated testing, Linux, AI (since 2018), and design patterns.
Parkour is for the more energetic peripatetic.
> Or is the idea that what you write to them and how you use them doesn't matter, it's all up to the model/harness?
Yes, it is. Source: had models inplement complex things from scratch and bullshit regardless of whether it was a one-line prompt or a detailed "SOTA witchcraft magic spells that are guaranteed to work"
It's not really a skill. The models are at this point smarter than you are, so the idea that you can prompt them "better" is laughable really when discussing frontier models.
It's like imagining you could "prompt" Richard Feynman to be smarter at Physics.
That is, for 99.9% of engineers, if you want the model to do a code review of your project, the best solution is to just ask Fable, "Hey Fable, do a code review of this project." Throwing in extra text like "think like a senior engineer", "ensure you focus on DRY principles, KISS, self documenting code, etc", doesn't make a difference.
These sorts of tricks used to work with dumber models, but now, like I said before, it's like thinking you can prompt Linus Torvalds into writing better C than he already can do.
FWiW
> it's like thinking you can prompt Linus Torvalds into writing better C++ than he already can do.
~ https://medium.com/@jankammerath/linus-torvalds-critique-of-...
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I find this very much untrue in my practice and my testing, where fable prompted to do a review found surface level issues, while prompting along the lines of "assume it's wrong, prove it's correct" found much more in depth and real issues.
"DRY" and "KISS" are, possibly, the least interesting principles to which a software designer might adhere.
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Understanding how to write good prompts (prompt engineering) is still very much a relevant skill if you want to effectively use LLMs. Harnesses aren't magic.
> Remember in 2023 when people thought "prompt engineering" would be the new software engineering and invested tons of time into learning CoT, ReAct, thread-of-thoughts, etc?
Prompt engineering was a GPT-3 era term, which couldn't understand instructions. Then ChatGPT came out in late 2022, which made actual prompt engineering superfluous.
No, people tried to sell “prompt engineering” services well beyond that — especially for image generating AIs, which where believed to need extra instructions like “professional lighting”, a name of a camera model, lens parameters, etc.
I think one day it will relatively plateau and then these approaches will be meaningful improvements in performance. But yes, pointless for now.
I still believe in writing good prompts or good instructions. Bad prompts can sometimes blow up the bill. A poorly written spec can waste a lot of tokens
Definitely. But the special knowledge how to talk to a certain model is usually not worth it.
Being able to write clear, always is.
Sure but lets not pretend this is engineering.
Then let's not pretend what software engineers have been doing for the last 50 years is engineering, either. But what else would you call it?
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Any current way lasts 3-6 months. What's currently the way, will evolve too.