Comment by Retric
2 days ago
The difference is you’d remember some of the context from reading the thing where an LLM is starting from scratch every single time it comes up.
2 days ago
The difference is you’d remember some of the context from reading the thing where an LLM is starting from scratch every single time it comes up.
I would remember the reply from the LLM, and cross references back to the particular parts of the RFC it identified as worth focusing time on.
I’d argue that’s a more effective capture as to what I would remember anyway.
If wanted to learn more (in a general sense) I can take the manual away with me and study it, which I can do more effectively on its own terms, in a comfy chair with a beer. But right now I have a problem to solve.
There are opportunity costs to consider along with relevance. Suppose you are staying at my place. Are you going to read the manual for my espresso machine in total or are you going to ask me to show you how to use it or make one for you?
In any case, LLMs are not magical forgetfulness machines.
You can use a calculator to avoid learning arithmetic but using a calculator doesn’t necessitate failing to learn arithmetic.
You can ask a question of a professor or fellow student, but failing to read the textbook to answer that question doesn’t necessitate failing to develop a mental model or incorporate the answer into an existing one.
You can ask an LLM a question and blindly use its answer but using an LLM doesn’t necessitate failing to learn.
There’s plenty to learn from using LLM’s including how to interact with an LLM.
However, even outside of using a LLM the temptation is always to keep the blinders on do a deep dive for a very specific bug and repeat as needed. It’s the local minima of effort and very slowly you do improve as those deep dives occasionally come up again, but what keeps it from being a global minimum is these systems aren’t suddenly going away. It’s not a friend’s expresso machine, it’s now sitting in your metaphorical kitchen.
As soon as you’re dealt with say a CSS bug the odds of seeing another in the future are dramatically higher. Thus optimizing for diminishing returns means spending a few hours learning the basics of any system or protocol you encounter is just a useful strategy. If you spend 1% of your time on a strategy that makes you 2% more efficient that’s a net win.
Sometimes learning means understanding, aka a deep dive on the domain. Only a few domains are worth that. For the others, it's only about placing landmark so you can quickly recognize a problem and find the relevant information before solving it. I believe the best use case of LLMs is when you have recognized the problem and know the general shape of the solution, but have no time to wrangle the specifics of the implementation. So you can provide the context and its constraint in order to guide the LLM's generation, as well as recognize wrong outputs.
But that's not learning or even problem's solving. It's just a time saving trick. And one that's not reliable.
And the fact is that there's a lot of information about pretty much anything. But I see people trying to skip the foundation (not glamorous enough, maybe) and go straight for the complicated stuff. And LLMs are good for providing the illusion that it can be the right workflow.
1 reply →
All of the above is true, but between solving quicker, and admitting we gave context:
I do agree with you that an LLM should not always start from scratch.
In a way it is like an animal which we have given the ultimate human instinct.
What has nature given us? Homo Erectus is 2 million years ago.
A weird world we live in.
What is context.