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A lot of people here haven't integrated GPT into a customer facing production system, and it shows gpt-4, gpt-4-turbo, and gpt-4o are not the same models. They

A lot of people here haven't integrated GPT into a customer facing production system, and it shows gpt-4, gpt-4-turbo, and gpt-4o are not the same models. They are mostly close enough when you have a human in the loop, and loose constraints. But if you are building systems off of the (already fragile) prompt based output, you will have to go through a very manual process of tuning your prompts to get the same/similar output out of the new model. It will break in weird ways that makes you feel like you are trying to nail Jello to a tree There are software tools/services that help with this, and a ton more that merely promise to, but most of the tooling around LLMs these days gives the illusion of a reliable tool rather than results of one. It's the early days of the gold rush still, and every one wants to be seen as one of the first

tmpz22 2y ago
Maybe we shouldn't be selling products built on such a shaky foundation? Like Health Insurance products for example. [2]: https://insurtechdigital.com/articles/chatgpt-the-risks-and-rewards-for-insurers https://insurtechdigital.com/articles/chatgpt-the-risks-and-... --- please disregard [1] it was a terrible initial source I pulled of Google [1]: https://medium.com/artivatic/use-of-chatgpt-4-in-health-insurance-12548f3ae2c5 https://medium.com/artivatic/use-of-chatgpt-4-in-health-insu...
- bcrl 2y ago
  Minimum Viable Products are pretty much by definition built on shaky foundations. At least with software written by humans the failure modes are somewhat bounded by the architecture of the system as opposed to the who-knows-what-the-model-will-hallucinate of AI.
  - nerdjon 2y ago
    I think that is the key problem, a traditional MVP is a mostly known entity. It may be missing some features, some bugs, etc. But it is an MVP not because it was necessarily rushed out the door (I mean... it was, but differently) but because it has some rough edges and is likely missing major features. Where what it seems we are getting with a lot of these companies shoving AI into something and calling it a product, is an MVP that is an MVP due to an unknown and untested nature.
    - mewpmewp2 2y ago
      But ultimately we have to test and release things to see what works and what doesn't. Very many usecases don't require perfect accuracy.
    - zerkten 2y ago
      The term MVP was cover for shoving poor quality software out on the market long before AI became involved. This is unfortunate, but inevitable when the term was popularized. AI is incredibly easy to tack on now, so people are doing that too.
      - nerdjon 2y ago
        That is true, but I think rushing to add AI features made it a completely different situation. We get a lot of MVP crap before, don't get me wrong. But at least it was understood crap. Sure it may have bugs in it and that is to be expected. But there was a limit in how wrong it could go. Since at the end of the day it was still limited to the code within the application and the server (if there is one). Meanwhile when an over-reliance on an LLM goes wrong, depending on how it goes wrong could be catastrophic. As we have seen time and time again just in the last couple months, when LLM's are shoved into something we seem to get a serious lack of testing under the guise of "beta".
- Sharlin 2y ago
  Building products on shaky foundations is a tried-and-true approach in IT business.
- benreesman 2y ago
  For a different point of view from someone with extremely credible credentials (learned this stuff from Hinton among many other things) and a much more sober and balanced take on all this I recommend the following interview with Nick Frosst (don’t be put off by the clickbait YouTube title, that’s a very silly caption): https://youtu.be/4JF1V2hzGKE https://youtu.be/4JF1V2hzGKE
- SkyPuncher 2y ago
  I’m not really sure this is an entirely fair argument. If you rely on third party packages of any type, you have dependencies that can rapidly and unexpectedly break with an update. Semantic versioning is supposed to help with this, but it doesn’t always help.
djohnston 2y ago
> It will break in weird ways that makes you feel like you are trying to nail Jello to a tree Probably the best description of working with LLM agents I've read
- visarga 2y ago
  It gets more interesting when you get to benchmarking your prompts for accuracy. If you don't have an evaluation set you are flying blind. Any model update or small fix could break edge cases while you don't know.
  - amluto 2y ago
    Make sure you don’t upload that evaluation set to any service that resells data (or gets scraped) for LLM training!
  - djohnston 2y ago
    We are using benchmarking on our own eval sets, which makes it easier to measure the variance that I’ve found impossible to eliminate.
- barrell 2y ago
  Came here to say the same thing, it sums it up perfectly
bbor 2y ago
My naive answer: turn away from Silicon Valley modernity with its unicorns and runways and “”marketing””, and embrace the boring stuffy academics! https://dspy-docs.vercel.app/ https://dspy-docs.vercel.app/
- tbarbugli 2y ago
  hosted on Vercel and Github...
- mdp2021 2y ago
  Is it Winter already?
  - astromaniak 2y ago
    It can't be, it's too big to die.
  - bbor 2y ago
    Oh god, what I would give for an AI winter right now… I think we’ve officially hit AI global warming
- stavros 2y ago
  I never got DSPy. I only tried a brief example, but can someone explain why it's better than alternatives? Not that I hold LangChain in particularly high regard...
- adamgordonbell 2y ago
  I've seen people mention this lib before and I have a hard time understanding the use cases Nad how it's used.
outside1234 2y ago
Hopefully you built a solid eval system around the core of your GenAI usage, otherwise, yes, this is going to be very painful :)

tmpz22 2y ago

Maybe we shouldn't be selling products built on such a shaky foundation? Like Health Insurance products for example. [2]: https://insurtechdigital.com/articles/chatgpt-the-risks-and-rewards-for-insurers https://insurtechdigital.com/articles/chatgpt-the-risks-and-... --- please disregard [1] it was a terrible initial source I pulled of Google [1]: https://medium.com/artivatic/use-of-chatgpt-4-in-health-insurance-12548f3ae2c5 https://medium.com/artivatic/use-of-chatgpt-4-in-health-insu...

bcrl 2y ago
Minimum Viable Products are pretty much by definition built on shaky foundations. At least with software written by humans the failure modes are somewhat bounded by the architecture of the system as opposed to the who-knows-what-the-model-will-hallucinate of AI.
- nerdjon 2y ago
  I think that is the key problem, a traditional MVP is a mostly known entity. It may be missing some features, some bugs, etc. But it is an MVP not because it was necessarily rushed out the door (I mean... it was, but differently) but because it has some rough edges and is likely missing major features. Where what it seems we are getting with a lot of these companies shoving AI into something and calling it a product, is an MVP that is an MVP due to an unknown and untested nature.
  - mewpmewp2 2y ago
    But ultimately we have to test and release things to see what works and what doesn't. Very many usecases don't require perfect accuracy.
  - zerkten 2y ago
    The term MVP was cover for shoving poor quality software out on the market long before AI became involved. This is unfortunate, but inevitable when the term was popularized. AI is incredibly easy to tack on now, so people are doing that too.
    - nerdjon 2y ago
      That is true, but I think rushing to add AI features made it a completely different situation. We get a lot of MVP crap before, don't get me wrong. But at least it was understood crap. Sure it may have bugs in it and that is to be expected. But there was a limit in how wrong it could go. Since at the end of the day it was still limited to the code within the application and the server (if there is one). Meanwhile when an over-reliance on an LLM goes wrong, depending on how it goes wrong could be catastrophic. As we have seen time and time again just in the last couple months, when LLM's are shoved into something we seem to get a serious lack of testing under the guise of "beta".
Sharlin 2y ago
Building products on shaky foundations is a tried-and-true approach in IT business.
benreesman 2y ago
For a different point of view from someone with extremely credible credentials (learned this stuff from Hinton among many other things) and a much more sober and balanced take on all this I recommend the following interview with Nick Frosst (don’t be put off by the clickbait YouTube title, that’s a very silly caption): https://youtu.be/4JF1V2hzGKE https://youtu.be/4JF1V2hzGKE
SkyPuncher 2y ago
I’m not really sure this is an entirely fair argument. If you rely on third party packages of any type, you have dependencies that can rapidly and unexpectedly break with an update. Semantic versioning is supposed to help with this, but it doesn’t always help.

djohnston 2y ago

> It will break in weird ways that makes you feel like you are trying to nail Jello to a tree Probably the best description of working with LLM agents I've read

visarga 2y ago
It gets more interesting when you get to benchmarking your prompts for accuracy. If you don't have an evaluation set you are flying blind. Any model update or small fix could break edge cases while you don't know.
- amluto 2y ago
  Make sure you don’t upload that evaluation set to any service that resells data (or gets scraped) for LLM training!
- djohnston 2y ago
  We are using benchmarking on our own eval sets, which makes it easier to measure the variance that I’ve found impossible to eliminate.
barrell 2y ago
Came here to say the same thing, it sums it up perfectly

bbor 2y ago

My naive answer: turn away from Silicon Valley modernity with its unicorns and runways and “”marketing””, and embrace the boring stuffy academics! https://dspy-docs.vercel.app/ https://dspy-docs.vercel.app/

tbarbugli 2y ago
hosted on Vercel and Github...
mdp2021 2y ago
Is it Winter already?
- astromaniak 2y ago
  It can't be, it's too big to die.
- bbor 2y ago
  Oh god, what I would give for an AI winter right now… I think we’ve officially hit AI global warming
stavros 2y ago
I never got DSPy. I only tried a brief example, but can someone explain why it's better than alternatives? Not that I hold LangChain in particularly high regard...
adamgordonbell 2y ago
I've seen people mention this lib before and I have a hard time understanding the use cases Nad how it's used.

outside1234 2y ago

Hopefully you built a solid eval system around the core of your GenAI usage, otherwise, yes, this is going to be very painful :)