3 ms·

No one is happy about the need for prompt engineering and other LLM hacks, but part of being a professional in the real world is doing what works.

bgwalter 1y ago
Software worked quite well before the plagiarizing chat bots. This is just a new fad like agile, where the process and tool obsessed developers blog and preach endlessly without delivering any results. For them "it works". The rest of us can either join the latest madness or avoid places that use LLMs, which of course includes all open source projects that do actual work that is not measured in GitHub kLOCs and marketing speak.
- rat87 1y ago
  I don't think software has ever worked "well".
th0ma5 1y ago
Being a professional also involves reading about the technology like how there are studies saying that prompt engineering has hit a limit which is why people are talking about reasoning, agentic systems, this vendor vs that vendor vs offline, etc... Part of being a professional is also wanting to understand why and how something works and not disclosing my secrets to a third party vendor or otherwise wish casting over a black box that I cannot audit. Part of being a professional includes a history of natural language processing experience and knowing the limits of the digital representation of information, and having played with the early incarnations of OpenAI products and seeing them fail in the same way ever since, in the well documented ways that transformers are known to fail. There's also a real problem in the overloading of terms where the lay people are hearing "we have to cross our fingers and hear rumors and the magic box will make things correct for us" vs. machine learning experts saying "you have to limit the context to precisely the problem at hand and then their accuracy will improve" except... in machine learning terms the second is actually about as good as the first and they are both bad ways of working. There is very exciting ongoing research operating on these things in parallel to just absolute mysticism and here on HN that line is pretty much eroded away, and a lot of that is due to thought-terminating responses that blame the users.

bgwalter 1y ago

Software worked quite well before the plagiarizing chat bots. This is just a new fad like agile, where the process and tool obsessed developers blog and preach endlessly without delivering any results. For them "it works". The rest of us can either join the latest madness or avoid places that use LLMs, which of course includes all open source projects that do actual work that is not measured in GitHub kLOCs and marketing speak.

rat87 1y ago
I don't think software has ever worked "well".

th0ma5 1y ago

Being a professional also involves reading about the technology like how there are studies saying that prompt engineering has hit a limit which is why people are talking about reasoning, agentic systems, this vendor vs that vendor vs offline, etc... Part of being a professional is also wanting to understand why and how something works and not disclosing my secrets to a third party vendor or otherwise wish casting over a black box that I cannot audit. Part of being a professional includes a history of natural language processing experience and knowing the limits of the digital representation of information, and having played with the early incarnations of OpenAI products and seeing them fail in the same way ever since, in the well documented ways that transformers are known to fail. There's also a real problem in the overloading of terms where the lay people are hearing "we have to cross our fingers and hear rumors and the magic box will make things correct for us" vs. machine learning experts saying "you have to limit the context to precisely the problem at hand and then their accuracy will improve" except... in machine learning terms the second is actually about as good as the first and they are both bad ways of working. There is very exciting ongoing research operating on these things in parallel to just absolute mysticism and here on HN that line is pretty much eroded away, and a lot of that is due to thought-terminating responses that blame the users.