For example, if you write iOS apps, you ask it to do some animations. It will do it for you, but you are at it's mercy of writing complete custom code or you can specify they use a certain apple supplied API for a native experience. It's one of thousand things that will get a vibe coder if they don't ask. Or you may get lucky and LLM chooses to use a native method.
LLMs reward expertise
541–550 of 607 posts
Re: LLMs reward expertise
#542The entire issue is that when you ask it to do something, you are leaving it to chance they may or may not do it properly, either on look/feel, performance, security, scalability etc. It compounds as you layer a new prompt output over that project.
Re: LLMs reward expertise
#543I agree with this post's gist, and I've certainly noticed how LLMs change their interaction with me once I demonstrate some knowledge. I've often started a technical conversation very vaguely and only once I challenge the LLM on its simplifications does it start to actually get to the meat of issues. Often there is a perceptible moment where the LLM seems to recognize my level of ability and how it communicates clearly changes.
But another thing I have found is that I get significantly better results from the LLM by treating it like an intelligent independent agent. All of the "you are a senior dev ..." or "your starving kids depend on the correctness of this answer ..." kind of prompting has been mostly useless. In general, I find being honest and clear to be the best strategy.
You can't "pretend" to be a senior software engineer. If I can root you out of an interview process then you aren't going to fool the LLM. But if you clearly state your level of expertise and your desired outcome, then the LLM does a very good job of meeting you where you are.
There is also a strange ephemeral attitude I get from agents sometimes, like they don't like to be called out for being wrong. But in the same way that human's show this trait, they also seem to warm up over time as they gain trust. It is almost like social positioning, once they realize they aren't actually expert they morph into a support role stance pretty seamlessly. That is also why they can still feel sycophantic, because once they realize they aren't actually driving the discussion they can actually feel like enthusiastic passengers, wanting to see where the conversation leads as much as the prompter.
Re: LLMs reward expertise
#544The amplifying mirror analogy works best here. LLMs are ultimately a reflection of your own interactions with its weights, the tone you use, the structure with which you construct your prompt, aspects of an issue you tend to focus on, your breadth of vocabulary and world knowledge and whatnot. People who (carefully) use it as an extension of their own mind and senses will very likely thrive, and those who use it as a…
Mind sharing the skill?
Re: LLMs reward expertise
#545Earlier quoted context omitted.
What makes this anecdote hard to believe is that seemingly two things happened simultaneously: 1. The layperson was able to to steer the session(-s) into full PM/PO mode ideating, refining and explaining features and ideas. 2. The user was the sycophant in this relationship, never steering the session(-s) into producing something tangible. The premise supposes that somehow the session(-s) never even tangentially touc…
Indeed. It reads like software engineer employment cope by appealing to the lowest common denominator possible. As if solutions for that type of end user weren't already solved for over a decade ago, most of which now have agents built in. It just is not believable or interesting. Even if it did happen, the reality is it just doesn't matter.
It reads like a discussion between two people sharing detailed experiences of concrete things they've observed, in response to an article where the author did the same. It could be that they're lying to stave off employment anxiety, I suppose, even though nobody involved seemed particularly anxious and everyone involved seems pretty familiar with AI tooling.
It could also be that you've seen a lot of social media memes regarding "cope", observed their ability to provoke strong emotions, and confused this for meaningful insight. I see a lot of AI commentary these days that is clearly being spread for its virality rather than its truth value.
Re: LLMs reward expertise
#546Earlier quoted context omitted.
> I might proactively steer newcomers towards your acronym as a kind of reclaiming. Any negative connotations "rice burner" once had was lost when the term shifted towards referring to cars instead of humans. But now re-recognizing that the enhancements are inspired by the East Asian race turns the connotations back to humans. Isn't that a regression?
Nah, I remember back then, it was definitely intended to be derogatory to the cars, to asians, and to people who like the cars.
Yes, the term originated as derogatory slang towards the Asian population in the early 1900s, and its use towards them became especially popular during the Korean War. There is no question there. However, as before, the term evolved away from people and towards cars. You cannot be derogatory towards an inanimate object. It doesn't have a mechanism to internalize feedback. In that evolution, the term lost its derogatory connotations. The historical usage, while a part of our past, is used no more.
Reintroducing this to be something about a population's race reinstates the derogatoriness. You can be derogatory towards humans. Minimizing the cosmetic enhancements to be being inspired by the East Asian race and not valuable human achievement brings us right back to the same place we were when Japanese cars started being introduced into the North American market, diminishing the human contribution. It is a regression.
Re: LLMs reward expertise
#547Sometimes after a larger source draft with AI before I even touch the program I start to pick up on some of the holes in my prompts caused bugs/unintentional mechanics to be potentially woven in. Knowing what you want and how it should be made is half of it, but if you don't supply the bot with extra guardrails eg. don't modify the contents of x, because of y don't allow z. etc. They will do what you ask of them, usually less than you'd hope.
Re: LLMs reward expertise
#548LLMs are good on things I know little about, but fall short on things I'm good in.
Re: LLMs reward expertise
#549I agree with this post's gist, and I've certainly noticed how LLMs change their interaction with me once I demonstrate some knowledge. I've often started a technical conversation very vaguely and only once I challenge the LLM on its simplifications does it start to actually get to the meat of issues. Often there is a perceptible moment where the LLM seems to recognize my level of ability and how it communicates clear…
That's some of the most weird anthropomorphization of a next token predictor I've read in a while…
This things output token which are correlated with the context given. That's all!
If you feed it some context the parrot will answer with the same.
It does not "sense your expertise level"—it just outputs correlated tokens… Is this really so hard to understand?
Re: LLMs reward expertise
#550Earlier quoted context omitted.
But how would you know? If you're an inexperienced developer you may well end up ingraining bad patterns without even realizing it.
Yes, how does the LLM make a difference there? Inexperienced developers write bad code. Hell, experienced developers who are outside their domain write bad code too, then you later realize that it's hard to read, or that it leads to architectural problems or such, and hopefully learn from the experience.
In the context of what I was replying to:
> This is a chicken and egg problem I find hard to resolve with LLMs. If we're pushed to delegate most work to them, how do you build that expertise?
Learning from LLM written code is significantly and meaningfully different from struggling on your own or learning from more experienced human co-contributors. Especially in a large, complex, iteratively developed codebase.
Worse, once you're in that situation you are now at the juxtaposition of: "I did this. I understand what my reasoning was, and now I know why it is wrong and how to fix it" vs. "An LLM did this, I don't know why it did this, I'm not sure what it was trying to do or what pattern it followed and I'm not sure how to make it better because I didn't write or understand the original implementation either"
Is it impossible to learn and gain experience this way? Not at all. But it's definitely not equivalent.