How do you handle work that involves building novel systems, where good prior art simply doesn’t exist?
I’m currently dealing with a project that involves developing systems where the existing prior art is either completely proprietary and inaccessible, or public, but extremely nacient and thus documented learnings are less developed than our own learnings and designs.
Many projects may have the primary objective of getting something working. But we don’t all have the luxury of being able to declare something working and walk away. I specifically have requirements around long term evolution of our project (I.e. over a 5-10 year time horizon at a minimum), plus long term operational burden and cost. While also delivering value in the short term.
LLM provide are an invaluable tool for exploring the many possible solutions to what we’re building, and helping to evaluate the longer term consequences of our design decisions, before we’ve committed significant resources to developing them completely.
Of course we could do all this without LLMs, but LLMs substantially increase the distance we can explore before timelines force us to commit.
Maybe the main problem is not solved yet, but I highly doubt that the subproblems are not. Because that would be cutting edge domain, which is very much an outlier.
Ah so what exactly do you mean when you say
> Most of my coding is fully planned to get to the end. The experiment part is on a much smaller scale (module level).
I would seem that these statements taken together mean you don’t experiment at all?
That means that I take time to analyze the problem and come up with a convincing design (mostly research, and experience). After that I've just got a few parameters that I don't know much about. But that doesn't mean that I can't build the stuff. I just isolate them so that I can tweak them later. Why? Because they are often accidental complexities, not essential ones.
> That means that I take time to analyze the problem and come up with a convincing design (mostly research, and experience).
Ah I think we’re finally getting somewhere. My point is that you can use LLM as part of that research process. Not just as a poor substitute for proper research, but as a tool for experimental research. It’s supplemental to the normal research process, and is certainly not a tool for creating final outputs.
Using LLMs like that can make a meaningful difference to speed and quality of the analysis and final design. And something you should consider, rather than dismissing out of hand.