"More artists than engineers": yes and no.
I've been working with Pandas and Scikit-learn since 2012, and I haven't even put any "LLM/AI" keywords on my LinkedIn/CV, although I've worked on relevant projects.
I remember collaborating back then with PhD in ML, and at the end of the day, we'd both end up using sklearn or NLTK, and I'd usually be "faster and better" because I could write software faster and better.
The problem is that the only "LLM guy." I could trust with such a description, someone who has co-authored a substantial paper or has hands-on training experience in real big shops.
Everyone else should stand somewhere between artist and engineer: i.e., the LLM work is still greatly artisanal. We'll need something like scikit-learn, but I doubt it will be LangChain or any other tools I see now.
You can see their source code and literally watch in the commit history when they discover things an experienced software engineer would do in the first pass.
I'm not belittling their business model! I'm focusing solely on the software. I don't think they their investors are naive or anything.
And I bet that in 1-2 years, there'll be many "migration projects" being commissioned to move things away from LangChain, and people would have a hard time explaining to management why that 6-month project ended up reducing 5K LOC to 500 LOC.
For the foreseeable future though, I think most projects will have to rely on great software engineers with experience with different LLMs and a solid understanding of how these models work.
It's like the various "databricks certifications" I see around. They may help for some job opportunities but I've never met a great engineer who had one. They're mostly junior ones or experienced code-monkeys (to continue the analogy)
"More artists than engineers": yes and no. I've been working with Pandas and Scikit-learn since 2012, and I haven't even put any "LLM/AI" keywords on my LinkedIn/CV, although I've worked on relevant projects.
I remember collaborating back then with PhD in ML, and at the end of the day, we'd both end up using sklearn or NLTK, and I'd usually be "faster and better" because I could write software faster and better.
The problem is that the only "LLM guy." I could trust with such a description, someone who has co-authored a substantial paper or has hands-on training experience in real big shops.
Everyone else should stand somewhere between artist and engineer: i.e., the LLM work is still greatly artisanal. We'll need something like scikit-learn, but I doubt it will be LangChain or any other tools I see now. You can see their source code and literally watch in the commit history when they discover things an experienced software engineer would do in the first pass. I'm not belittling their business model! I'm focusing solely on the software. I don't think they their investors are naive or anything. And I bet that in 1-2 years, there'll be many "migration projects" being commissioned to move things away from LangChain, and people would have a hard time explaining to management why that 6-month project ended up reducing 5K LOC to 500 LOC.
For the foreseeable future though, I think most projects will have to rely on great software engineers with experience with different LLMs and a solid understanding of how these models work.
It's like the various "databricks certifications" I see around. They may help for some job opportunities but I've never met a great engineer who had one. They're mostly junior ones or experienced code-monkeys (to continue the analogy)