Comment by mindwok
4 days ago
Gotta be honest, almost every "how to use AI" resource seems pointless to me. I'm either going to ask the AI how to do it, or if it's about using the AI then we can just bake it into the harness or wait for Anthropic/OpenAI to do it for me because they're always trivial.
All of these resources on agentic workflows, managing agent memory, harness engineering, etc. appear to just be theatre to me.
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.
12 replies →
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.
11 replies →
Nah, you might be confusing prompt engineering with having domain knowledge. :)
3 replies →
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.
1 reply →
> 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.
3 replies →
> 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.
8 replies →
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.
2 replies →
Any current way lasts 3-6 months. What's currently the way, will evolve too.
Not only that, but all of this tooling around models has such a short shelf life as the models themselves grow in capabilities, they absorb the tooling. We've already seen it over and over again.
Amen on both your and OP's comments.
These tools work pretty well out-of-the-box. I'm sure I could squeeze out better token usage or streamline some tool calls, but it's not something I really want to focus on. Just like I don't want to endlessly configure my IDE, I don't really have patience with spending time on anything besides actually building something.
There was an article by Vercel on how ineffective tool calling is compared to just a single md file with clearly defined instructions. They showed a clever way on how to use compressed indexes. My experience with tool calling was similar to what Vercel described, and I spent hours trying to perfect it. Since reading Vercel’s findings, Claude.md and Agents.md, maybe a Project.md it’s all I use.
Do you have a link?
https://vercel.com/blog/agents-md-outperforms-skills-in-our-... perhaps?
>I'm either going to ask the AI how to do it
LLMs seem terrible at using LLMs in harnesses. Have you seen how they rot their context with the stuff they put in .md files if you let them?
You'd have to have the LLMs search, and thus these resources could be for them more than you
Yes, but I have had moderate luck with creating an "agent-instructor" skill that has strict instructions around keeping language strong, unambiguous, concise, and always presenting me with exact diffs to review before writing anything.
Another thing in it is a strict line count. Any increase in line count requires my approval. That last one is important because it plays well with two biases: models don't tend to create long lines so they won't try to cheat that way, and they're strongly inclined to keep churning out lines so I take that away from them.
Did you tell Claude "make an agent instructor skill, whatever you think is good, go for it," or did you use the knowledge you had gained about how AI works and how to write good instructions for it?
2 replies →
> I'm either going to ask the AI how to do it
First you have to know that "it" exists and is possible. A cookbook like this introduces readers to concepts and features they didn't know were there in the first place.
That's a fair response. I could see how some folks would benefit. I suppose what bothers me more is that they don't actually seem pedagogical in nature, they seem promotional.
Thanks for saying the quiet part out loud. Everytime I do a demo it seems like a waste of time compared to just actually building.
Unless I'm doing something super complicated even taking time to set things up like subagents, etc. seems like a waste compared to just building.
The only things that really seem beneficial (for Claude Code) seems to be learning to set up loops, memory and finding relevant MCP servers.
Exactly. I think we all have to come to this conclusion ourselves, because the message we’ve been getting from the LLM companies is “sweet user, you DO add value to the LLM, you gave it that custom subagent, remember?”, and then they go and roll that idea into the next version. Once they do that a few times you go “eh why would I bother, this will be a default soon. I’ll just prompt it like normal”.
Just use the vanilla settings.
Right now I feel like I'm getting so much value out of working daily with Fable, but I'd be embarrassed to share my sessions because they're pretty much how I would talk to a colleague. Spelling mistakes and half-baked thoughts included. Gone are the formal sounding prompts and specs I was proudly writing 6 months ago. But it's getting the results.
Once you understand enough to spawn independent agents for discrete tasks, it's hard to justify investing brain space into some harness that will likely be obsolete next week, if not tomorrow. And by the time the harness game converges, I suspect most of the high-end models will behave like them! Intrinsically.
This is how I've gone about it with my Shopify site.
I appreciate there may be more efficient ways to do things if you're an actual developer or engineer but I'm not and the tools I've been able to build so far have been both fun to make and add value to our workflow as a small 2 person ecommerce business.
I do at least have a background in web as a designer for many years and having worked with developers I can at least spec and understand how a product might work which is one thing Claude/AI isn't hugely helpful with and often it's the testing and QA phase as always where the problems and shortcomings expose themselves.
+1, by the time everybody sworn about MCPs, skills became a more efficient alternative. People learned to aggressively context manage, then longer context windows and agentic made that a lot less important. If the technique is any good, it will be baked in the next version. My philosophy is to avoid plugins / MCPs unless strictly required, and to have thorough prompts. This way I’ve successfully avoided to be in the way of progress by forcing obsolete optimisations on my LLMs
Yeah 100% agree, I would even go as far as it's kind of harmful it seems to just pollute the context or stagnant as the models and tooling changes so rapidly or it is just better to do something bespoke for your own project.
It was more important with earlier models, as they were less RL'd to golden paths, so the context could steer them more.
Now process engineering is more important than prompt engineering.
if you know how to ask Ai about how to use Ai, you already know how to use Ai
the point of guides is to provide assurance for people unfamiliar to the process in the first place
If the bar is knowing how to type a question into a box, I'm confident almost everyone is better off starting with that then reading a "cookbook" that starts with installing python packages.
I've see people write "broooo pleaseeee!" into the box. Something about the universe providing better fools.
3 replies →
I suspect certain workflows like langchain and others like it will retain usefulness into the future. Having deterministic steps before and after the llm is the way to go for anything that might be potentially harmful. Which I guess is what the harness does, but why be limited to a generic harness when we can use it to make specific ones for our needs.
Yea i do the same. I can't be bothered to watch videos and courses on how to use claude better when I can simply ask it myself.
If these things are so smart why do I have to coax it to be useful with all these magic spells scrawled on markdown parchment.
Ok, thanks, because I looked at one example and was like, “I am supposed to take someone’s OpenAPI doc and translate it back to English for the model?” And if they’re implying I should have an AI do that, why don’t they just build that step into the models rather than having someone prompt an AI to write these half-ass docs?
Maybe this “how to use ai” isn’t written for you, but for the agents who then have many ideas on what to do.
Not only that, but they change on a whim with new ideas on how to do things every few weeks.
This is also where I'm at. "Just ask the model stuff" continues to be the best, most durable advice. Karpathy himself had something recently about this on Twitter.
I appreciate the cookbooks bc sometimes I learn about ways to use ai that I did no know before.
I agree, there isn’t much of a skill to using AI. And the model keeps changing so frequently anyway making any/ old skills redundant.
I think there's "how to use AI" and "how to make frequent AI use economical in systems". Different things?
I think the main thing of interest in the linked site is the dates. You can quickly get a view of what was possible and when.
I found them handy to bring along coworkers who aren’t quite up to speed.
You don't learn about progress if you don't take part in it.
Before /goal was ralph-wiggum it was def an interesting learning experience, it was interesting to see how Claude became a lot better doing this itself but it still took 6 Month and more.
You can wait and sit it out and suddenly you get fired if you miss the point when to start spending more time and energy on topics like this.
[flagged]