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Is building in Generative AI risk?

1 points by yashthakker ↗ HN
With models constantly evolving and supporting new use cases, small startups are constantly pivoting to doing newer things in AI. It's only a matter of time before these new use cases are supported by the upcoming models.

My question is - how does a small company keep up to speed, with cash crunch? It's really a chaotic time in ai right now. What are your thoughts?

1 comment

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adamhp
The interesting thing about AI is if sending a request to a prompt is your backend, then you can change your backend in a matter of minutes by doing some simple prompt engineering. I think as the consistency improves, it's going to become really interesting. There's actually untapped value there in how it negates the risk of change by making it incredibly easy to update once it changes. This is obviously ignoring any API changes, etc.

Like I recently wrote a little AI wine pairings tool for a fun weekend project (https://oeno.chat) and it was originally GPT3.5, but I've tried similar prompts with GPT4 and it's just a better (although a little slower) version of the same information. I didn't really have to update anything, because I just ask it for specific JSON keys to be returned every time. Every now and then there are some artifacts where parts of the response bleed over, but it's just a toy project.

I'm really excited about leveraging existing tech by gluing it together with AI. Something like: first, get travel recommendations and extract all place entities (give me json); second, take all those and query some external geolocation api; third, build a pretty ui to map those places; last, integrate with Duffel or other booking API to actually book travel.

This is just a random example but I really see the initial leverage being in creativity of gluing together APIs and systems using LLM products to glue things together.

I think there's going to end up being a premium on front-end development until the AI can do better.