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73% of AI startups are just prompt engineering

pub.towardsai.net

121–130 of 212 posts

Re: 73% of AI startups are just prompt engineering

#123

> just prompt engineering This dismisses a lot of actual hard work. The scaffolding required to get SOTA performance is non-trivial! Eg how do you build representative evals and measure forward progress? Also, tool calling, caching, etc is beyond what folks normally call “prompt engineering”. If you think it’s trivial though - go build a startup and raise a seed round, the money is easy to come by if you can show res…

The money is easy to come by because wealthy investors, while they don't want to pay any more in taxes, are desperate to find possible returns in an economy that sucks outside of ballooning healthcare and the AI bubble... not because they need the money but because NUMBER MUST GO UP.

And more so than even most VC markets, raising for an "AI" company is more about who you know than what results you can show.

If anyone is actually showing significant results, where's the actual output of the AI-driven software boom (beyond just LLMs making coders more efficient by being a better google)? I don't see any real signs of it. All I see is people doing after market modifications on the shovels, I've yet to see any of the end users of these shovels coming down from the hills with sacks of real gold.

Re: 73% of AI startups are just prompt engineering

#124
post #60

Earlier quoted context omitted.

Well it took me 2 full-time weeks to properly implement a RAG-based system so that it found actually relevant data and did not hallucinate. Had to: - write an evaluation pipeline to automate quality testing - add a query rewriting step to explore more options during search - add hybrid BM-25+vector search with proper rank fusion - tune all the hyperparameters for best results (like weight bias for bm25 vs. vector, ho…

Sounds like you vibe coded a RAG system in two weeks, which isn't very hard. Any startup can do it. I've debugged single difficult bugs before for two weeks, a whole feature that takes two weeks is an easy feature to build.

I already had experience with RAG before so I had a head start. You're right that it's not rocket science, but it's not just "press F to implement the feature" either

P.S. No vibe coding was used. I only used LLM-as-a-judge to automate quality testing when tuning the parameters, before passing it to human QA

Re: 73% of AI startups are just prompt engineering

#126
Isn’t this true for most start ups out there even before AI? Some sort of bundle/wrapper around existing technology? I worked auditing companies and we used a particular system that cost tens of thousands of dollars per user per year and we charged customers up to a million to generate reports with it. The platform didn’t have anything proprietary other than the UX, under the hood it was a few common tools some of them open source. We could have created our own product but our margins were so huge it didn’t make sense to setup a software development unit not even bother with outsourcing it.

Re: 73% of AI startups are just prompt engineering

#130

Earlier quoted context omitted.

He may be real, but the article is fake BS. There is simply no way he'd be in a position to intercept the calls, and he never explains it.

There is nothing difficult about monitoring network traffic in this way for desktop or native apps.

Did you read the article? It claims to have knowledge of network traffic between the startup's devices and the AI providers' devices.
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