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Comment by ixsploit | Hacker News Reader

I like to use them to generate me tutorial series for technical topics I want to learn or improve. So I start a prompt like this:

> I want you to create a tutorial series about X for me. The prime objective is that I improve in topic X so never provide a solution but guide and teach. (for programming never write code). First create a question catalog to assess my current level.

Then I would ask it to structure the tutorial challenges in the following way: - Goal - Concept - Instructions

I figured that if I don't need to read any additional material on the topic the LLM is giving me too much information and I need to change the prompt. Works for me and I used this too learn topics I feel now comfortable with, like nushell, opencyper, elisp, boot loaders etc. But maybe you don't consider this "complex"

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Curious: Why not pick up a book or two on the topic, and use the LLM to help you through it [0]? To me, prompting the LLM without grounding it sounds like a sure shot way to end up learning "pop-sci", as GP puts it, instead of the actual science?

If today's top LLMs are reliable enough (without grounding) to academically learn "complex topics" from, may be I need to adjust my priors. I must say, I do find myself chatting about other topics (without the need for grounding) that I'm trying to "absorb" (not really learn), like Behavioural Psychology & Philosophy.

[0] Products like NotebookLM are built specifically for such usecases.

I use books to accompany my learning, but I really need something that _forces_ me to think and solve problems in that space on my own. Books can sometimes give me the illusion of learning something, but then, when needed, I've notice that I haven't really learned it.

LLMs give me structure based on my current skill level. And basically always I accompany this with books, I love reading. It's a nice combination for me.

I think the key difference here is that you're using the LLM to create exercises, not to replace the learning material

_Doing_ is probably one of the best ways to learn something.

"The best way is to learn"

From the hilarious "There are 3 ways to learn. (Knowledge Fight Animated)"

https://www.youtube.com/watch?v=NsqZZiWDHAQ

I would love to see the "depth" of your knowledge in those topics.

You literally proved the OP's point ... thinking you're learning. More like scratching the surface, with lots of invalid data while not being able to recognize what's invalid.

It's like with latest vector of attacks being spamming Github with malware injected in proper looking code in hope of AI to index it.

Then you paste the code because you don't understand it, but you take it as working and only doing what you've asked for.

This feels like an impossible assessment - yes, a model probably can't give you the education that a advanced/expert book on a topic will, but implying that having a verifiable goal is somehow fake learning feels like an intractable problem.

What level of evidence would be sufficient for you to accept that a model may be able to teach a concept?

I'm happy to take on this challenge with a topic of your choosing, but I don't believe there will be an evidence base that satisfies you that the knowledge is earned or deep enough.

I really wonder where this assessment is coming from? It's not that I use LLM written code for something (in those exercises at least), in fact I don't let the LLM write code (see my prompt example).

It's about guiding me in _doing_ exercises so I learn and I can evaluate if I learned something if I can apply the learning myself.

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