> warmth and empathy don't immediately strike me as traits that are counter to correctness
This was my reaction as well. Something I don't see mentioned is I think maybe it has more to do with training data than the goal-function. The vector space of data that aligns with kindness may contain less accuracy than the vector space for neutrality due to people often forgoing accuracy when being kind. I do not think it is a matter of conflicting goals, but rather a priming towards an answer based more heavily on the section of the model trained on less accurate data.
I wonder if the prompt was layered, asking it to coldy/bluntly derive the answer and then translate itself into a kinder tone (maybe with 2 prompts), if the accuracy would still be worse.
Alternatively, I've gotten exactly what I wanted from an LLM by giving it information that would not be enough for a human to work with, knowing that the llm is just going to fill in the gaps anyway.
It's easy to forget that the conversation itself is what the LLM is helping to create. Humans will ignore or depriotitize extra information. They also need the extra information to get an idea of what you're looking for in a loose sense.
The LLM is much more easily influenced by any extra wording you include, and loose guiding is likely to become strict guiding
Absolutely, I can see how that could be effective. The jobs may lean toward electrical experience. Power engineering is a subfield of electrical and may be relevant. I looked into it once for myself, seemed like a good fit.
Another field of tools that I'm looking at are the Geospatial ones. Being able to work with mapping software/data always felt like a good mix to me.
What tools are they teaching now?
I studied on like AMPL for linear/nonlin prog, ARENA for sims, Matlab for general but it's been a while.
Hey there! I have a B.S. in Information & Systems Engineering, which includes Operations Research (OR). Happy to chat about this!
When I graduated, the OR term was already fading, and from what I’ve seen, it’s pretty much gone as a standalone field. The tools are still strong, but OR isn’t often listed as a job specialization on its own.
I started as a business analyst, and while OR wasn’t in any job descriptions, it gave me an edge. I used OR methods to go above and beyond, working closely with branch and executive management to analyze cost-effectiveness, optimize decisions, and make strategic recommendations. This helped me stay at the top of my pay band. Of course, I still handled traditional BA tasks like dashboards, reports, automation, and SQL.
My advice? Cross-specialize. OR is incredibly valuable, but it works best when paired with another strong skill set. For me, a CS minor and SQL/database skills helped early in my career.
To put it simply: OR lets you optimize a warehouse layout—but most jobs also require you to move boxes. It aligns more with engineering management roles than entry-level work, and those management positions typically go to people with industry experience.
That said, I genuinely believe OR is one of the best specializations when combined with another field. You just need to polish it with the right complementary skills.
(Full disclosure I used AI to clean up this message, but it's still very close to my initial draft. Mostly just some grammar and phrasing changes, but it does kind of read like AI now so I wanted to call it out that the sentiment is still genuine)
As far as connecting to other practitioners, I mostly just stay active in forums and joined a few LinkedIn groups but I need to improve in this area too, which is my motivation for posting this.
Anecdotal but I told chatgpt to include it's level of confidence in its answers and to let me know if it didn't know something. This priming resulted in it starting almost every answer with some variation of "I'm not sure, but.." when I asked it vague / speculative questions and then when I asked it direct matter of fact questions with easy answers it would answer with confidence.
That's not to say I think it is rationalizing it's own level of understanding, but that somewhere in the vector space it seems to have a Gradient for speculative language. If primed to include language about it, it could help cut down on some of the hallucination. No idea if this will effect the rate of false positives on the statements it does still answer confidently however
No comment on if output analysis is all that is needed, though it makes sense to me. Just wanted to note that using file size differences as an argument may simply imply transformers could be a form of (either very lossy or very efficient) compression.