I have lengthy conversations with my LLM, almost like an interview. I agree on the expertise part, because I wouldn't be able to go in depth on a subject with it if I lacked the expertise. Some work is a result of design and negotiations in those designs.

I don't think Tao's style works with everyone/thing, especially if we don't know what style he's tuned his LLM on.

I agree. When I talk to LLMs about fields I am familiar with, I can push back on bad suggestions and ignore faulty/incorrect advice and assumptions, which is much harder for unfamiliar subjects.

Of course, simple common sense and extremely basic Googling on unfamiliar subjects can produce similar results, but it's much faster if you are truly understanding what the AI is suggesting.

I often do my best to represent a genuine interest in the subject at hand and learning in general to models. Imagine the model's response prose and mannerisms being on the other polar end of answering questions simply to get the correct answers as they're often scoped for on quantitative benchmarks. Not sure I explained this well, sorry. An LLM could help

I agree with the premise that LLMs reward experstise, but people without expertise can very eaily learn to prompt correctly and get to a result that is very good. I remember somebody proved a mathematical conjecture by just asking 'keep going' in plain english without a mathematics background.

I swear the ever living shit out of LLMs for even the tiniest of logical mistakes they commit.

Correcting LLMs with extreme swearing that they dare never make it again.

I make otherworldly progress with kimi, Gemini, Chatgpt, Deepseek and Claude.

Claude now stops the session. Hence Claude is now useless for me.

Swearing is nothing personal. Its a correctness enforcer.

The 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.

Keep telling yourself that, right up to the layoff.[1]

[1] https://www.linkedin.com/posts/ademola-adelakun_pov-you-get-...

So we could run a lighter LLM in front of humans, which translates from 'no domain knowledge' to 'domain expert' and in turn prompts over to the larger LLM.

Then the larger LLM gets all the right lights on, yields better outputs and we translate back into user domain.

I kinda thought the chain-of-thought reasoning already did this, no?

This has been my experience as well. I’ve also been thinking a lot about Terrence Tao and his chats and presentation.

This is true today and has been my experience as well -- both to write software as well as doing computational physics.

The interesting question then is to ask how long will this stay true? As the models get better will they eventually not need the human expertise to start adding value?