If you're trying to use LLMs in an enterprise context, you would understand. Switching models sometimes requires tweaking prompts. That can be a complete mess, when there are dozens or hundreds of prompts you have to test.

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mr-pink 2 hours ago | parent | next [-]

sounds like job security. be careful what you wish for before you get automated

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bethekidyouwant 3 hours ago | parent | prev [-]

This sounds made up. Much like “prompt engineering” Let’s hear an actual example

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Koffiepoeder an hour ago | parent | next [-]

We have an OCR job running with a lot of domain specific knowledge. After testing different models we have clear results that some prompts are more effective with some models, and also some general observations (eg, some prompts performed badly across all models).

Sample size was 1000 jobs per prompt/model. We run them once per month to detect regression as well.

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gwd an hour ago | parent | prev | next [-]

OK, so a while back I set up a workflow to do language tagging. There were 6-8 stages in the pipeline where it would go out to an LLM and come back. Each one has its own prompt that has to be tweaked to get it to give decent results. I was only doing it for a smallish batch (150 short conversations) and only for private use; but I definitely wouldn't switch models without doing another informal round of quality assessment and prompt tweaking. If this were something I was using in production there would be a whole different level of testing and quality required before switching to a different model.

	▲	0xbadcafebee an hour ago \| parent [-]
		The big providers are gonna deprecate old models after a new one comes out. They can't make money off giant models sitting on GPUs that aren't taking constant batch jobs. If you wanna avoid re-tweaking, open weights are the way. Lots of companies host open weights, and they're dirt cheap. Tune your prompts on those, and if one provider stops supporting it, another will, or worst case you could run it yourself. Open weights are now consistently at SOTA-level at only a month or two behind the big providers. But if they're short, simple prompts, even older, smaller models work fine.

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mcint 2 hours ago | parent | prev [-]

Enterprises moving slow, or preferring to remain on old technology that they already know how to work...is received wisdom in hn-adjacent computing, a truism known and reported for more than 3 decades (5 decades since the Mythical Man-Month).

Sounds like someone who's responsible, on the hook, for a bunch of processes, repeatable processes (as much as LLM driven processes will be), operating at scale.

Just in the open, tools like open-webui bolts on evals so you can compare: how different models, including new ones, perform on the tasks that you in particular care about.

Indeed LLM model providers mainly don't release models that do worse on benchmarks—running evals is the same kind of testing, but outside the corporate boundary, pre-release feedback loop, and public evaluation.

https://chatgpt.com/share/69aa1972-ae84-800a-9cb1-de5d5fd7a4...