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shouldn't your willingness to pay/not pay be a function of your experience and the value the product delivers you, rather than an implementation detail such as
by thornewolf 2y ago
shouldn't your willingness to pay/not pay be a function of your experience and the value the product delivers you, rather than an implementation detail such as fine tuning vs prompt engineering?
I think this difference should be comparable to asking something like:
This website uses UsersDb, which makes me think you have built a custom database, but if you can easily switch between sqlite and postgres, that makes it seem like there's no real work happening. Could you shed some light on this?
I feel this comparison is almost entirely valid since fine tuning can be as simple as:
- Create request/response tabular data
- Click "upload" on leading LLM provider
- Click "fine tune"
- Change your code to reference llm.com/finetuned instead of llm.com/base-model
- sabbaticaldev 2y ago> shouldn't your willingness to pay/not pay be a function of your experience and the value the product delivers you, rather than an implementation detail such as fine tuning vs prompt engineering? bullshitting/lying isn’t implementation detail, it’s a mindset and I understand people who actively try to avoid it. Prompts and rag don’t make it a new powerful model that is superior to say ChatGPT
- splittydev 2y agoYeah, that's basically what I was thinking. I didn't wanna use the word "lying" as to not appear super confrontational, but when someone claims it's using a model called "langcss.fast", it should be safe to assume that they're actually using a custom model. It's disingenuous at best. I'm not even saying there is any need for a custom model. Obviously, RAG etc. can work just as well, or even just a well-crafted prompt on a foundation model. But selling it as a custom model is misleading on purpose, and deters me from using the product.