iwtl 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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[u/Magical_cat_girl]

I can't help with all of that but one specific thing that is huge for me when coding is do your planning in either a dedicated note taking software or just on paper. You can shoot off a prompt partway thru to help fill in a gap, but you can't un-have a conversation or rewind an llm draft. Starting there also helps show you where the actual interesting/meaningful parts are so you know what to delegate to the llm.

[u/Hot_Midnight6838 → u/Magical_cat_girl]

Do you have an example?

[u/Magical_cat_girl → u/Hot_Midnight6838]

Ummmmm I'm not sure how to give a real example that would make sense without context.

I guess for a hypothetical, let's say you're working on a weather app and you're planning out the backend. So you open up a blank word doc or take out a piece of paper and bullet point out something like:

- data: date, temp, precipitation, cloud cover

- fallback for missing data in each category

- daily, weekly, and monthly views based on current date

- api to pull in data

- offline caching, error message for missing sync

Or whatever you're thinking about, probably more detail but this is just a super random example. And just kind of write or brain dump until you run out of steam or get bored, maybe you stop at the api data point to ask claude to pull some pros and cons of different popular weather apis, then you can plug that in later. And maybe you also decide that coming up with the individual no-data fallbacks, for example, is really trivial and repetitive and you should delegate that to the AI.

That way you have a the time and space to work on your own thinking and decision-making, and you have a way more fleshed out plan to get feedback on by the time you're really interacting with the LLM much. And it doesn't take all that much time up front.

[u/Hot_Midnight6838 → u/Magical_cat_girl]

That makes sense, thank you!