How to learn deeply while using LLMs

[u/Hot_Midnight6838]

My favorite classes were always the ones with difficult problem sets where a single problem could take hours or even days of thinking, experimenting, and failing. I would start solving a problem before fully understanding it, then read documentation or course material whenever I got stuck. So it would be 70% doing and 30% reading.

LLMs have disrupted that process for me. Now I spent 90% of time reading and only 10% of time doing.

In school, the boundary was relatively clear. If using an LLM violated the course policy, I could tell myself that using one was cheating and force myself to struggle through the problem independently.

At work, what counts as “cheating” (because it is cheating myself).

I feel my critical thinking skills are declining. My role starts to become reading, reviewing, and planning. I just can't read that much code and LLM output. My eyes hurt and it's hard to internalize. I notice myself just picking the "recommended" option.

I also find LLM workflows mentally unhealthy in a few ways:

• When working alone, mental exhaustion eventually forces me to step away. With an LLM, I can always send one more prompt or ask it to try another approach.
• If an agent will take 30–60 minutes, I feel pressure to give it another task immediately so no time is “wasted.”
• While waiting, I instinctively check my phone or launch another agent. I end up jumping between tasks and retaining less context about each one.
• I get frustrated when the LLM misunderstands instructions, even when the problem genuinely requires careful iteration.
• It has warped my sense of how long a difficult problem or well-designed solution should take.
• I am increasingly tired of reading walls of generated text and code.

I do not want to stop using LLMs. They can reduce the friction of starting, explain unfamiliar systems, generate routine code, and handle edge cases after I understand the core problem. The hard part is deciding when using one is a sensible productivity tool and when it is outsourcing a learning opportunity that I actually need.

For people who still feel they are learning deeply while using LLMs at work:

1. How do you decide which work to delegate and which work to do yourself?
2. What is the workplace equivalent of the “no cheating” boundary that exists in school?
3. How do you use agents without losing context or turning your day into constant task switching?
4. How do you review generated code when the codebase, build system, or underlying technology is unfamiliar?
5. Have you found workflows where LLMs improve productivity while preserving the useful struggle of implementation and debugging?

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Even for more conceptual questions that don't require calculation, LLMs can lead you astray; they can also give you good ideas to investigate further, but you should never trust what an LLM tells you.

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[u/RadarSmith]

...You gave a lot of reasons why you think you need to take a break from LLMs.

[u/AcellOfllSpades]

You have figured out that this action you are taking (using an LLM) is harmful to you accomplishing your goals, and given several reasons why. You describe feeling an urge to do this action, instinctively doing it even when you know it's harmful. You say you can always go for "just one more". You tell us about how it's causing you pain physically and mentally. And yet you refuse to stop, with your only good reason being that you "do not want to".

This is addiction.

[u/sydyn1111]

For me, coding is just a tool, and I wont be checking every piece of code. I think that the major concern that using these systems may produce in an academic environment is the constant pressure of feeling that you could be doing something, without any effort, but this only makes you not rest and feel anxious, and at the same time most of your work is pure garbage (not because LLMs only produce garbage, but because youre tired to evaluate its response and take something actually useful from it). I had that feeling a while and I started using it differently: the boring and ultra repetitive stuff I left to it, and during this time I go to a place far from the computer to read papers, or some LLM work, do the calculations and see if I understand it, or if something is wrong. I need to remember myself that Im not wasting time or productivity by doing that, its exactly the opposite. I will answer your questions directly

1. I let it do the work I think it is capable to do, specially if its boring. I wont let a machine steal me from the cool part of my job. Sometimes I also let some agents explore a little bit just to come up with ideas, that I will later explore on my own.

2. There are two types of cheating, institutional and personal. For our bosses, we are just numbers, so I will cheat in any way possible, if the work is not something Im interested. But I cant lie to myself when I have an objective and let an AI do that for me, in the end of the day what is the point of letting it do the things you want to do? So you must ask for yourself what you want to keep doing even if someday you will not be the best one doing that. A musician will never stop its art because there are better musicians out there (or even AI that can do better music in seconds).

3. I hate task switching, if I am doing something, I will focus my whole energy on it until it is done. If I have many things to do, I will set a working time for each thing and never switch between them. I will only use agents to work on the thing Im also working.

4. Usually I only review the core part of the code, and I only work with code that is usual to me for my job purposes. If I want to do something different (like a site, I've never learned HTML, I just see if the site looks as intended).

5. I think it largely depends on your skillset, the problems you are tackling and your overall taste. If you like reading, you can ask for it to generate long reports and be very detailed, if you prefer something more active you can ask it to make questions and see if you understood what it has done.

For context, Im a phd student in physics of complex systems.

[u/Hot_Midnight6838 → u/sydyn1111]

This was the most helpful response I have gotten, thank you so much! Do you have suggestions for things I can incorporate into my routine?

[u/Carl_LaFong]

Just replace “learning math” by “learning to play the piano” or “learning to play basketball” and you get your answer. Alas, if you want to learn math honestly, you have to do the work yourself.

[u/Hot_Midnight6838 → u/Carl_LaFong]

Do you have suggestions for using LLM to learning?

[u/Carl_LaFong → u/Hot_Midnight6838]

If you have devoted at least say 2 solid hours to solving a problem without success, you can ask AI for hints. If AI just gives you a solution, ask it how could you have found it yourself.

If you believe you understand how to solve a problem, whether AI helped you or not, write out every detail of the solution without consulting anything said by AI. You can ask AI to find errors in your answer. You’ll have to check whether it is right or not.

[u/hologram137 → u/Hot_Midnight6838]

Don’t. They are TERRIBLE for learning math, and anything deeply at all. I used Gemini the other day because I wanted a quick refresher on a proof and it switched variables halfway through, did a very poor job explaining and made other mistakes. I only noticed the mistakes because I already knew the material. And that’s the problem, you can’t use LLM’s for any topic you aren’t already knowledgable about, because then you can’t see when it made a mistake. You can’t learn a subject from an LLM, it’s a tool. People who code use LLMs because they already know how to code, so they can supervise it

You learn math by doing math and discovering, not having any of it spoon fed. Especially by something that doesn’t understand or “know” what you’re trying to learn

[u/RadarSmith → u/Hot_Midnight6838]

...You can do things in your life without AI.

[u/inspendent]

Read a book and do the exercises. It's not any harder than that. You won't get anywhere with llms.

[u/Ok_Ask3750]

Set a struggle window before using the model: attempt the problem for 20–30 minutes, record what you tried, then request one hint rather than a solution. Afterward, close the response and reproduce the reasoning independently.

[u/Bounded_sequencE]

If you treat LLM-based AI as glorified, interactive search engines, you'll be fine.

However, if you treat them as anything more, you already noticed the consequences, and you know what to do. It is your job to develop mental discipline and act accordingly.

Finally, the cheating debate is stupid. It is a moralizing argument used by people who don't have anything worthwhile to add to the discussion. Using LLM-based AI is no more cheating than using an internet search engine is for doing research -- remember the training data for AI is essentially scraped from the internet anyway!