Where is this guy sitting that he is able to collect all of this data? And why is he able to release it all in a blog post? (my company wouldn't allow me to collect and release customer data like this.)
Im also wondering how he is able to see calls to AI providers directly in the browser, client side api calls? Thats strange to me. Also how is he able to peer into the rag architectures? I don’t get that, maybe GpT4.1 allows unauthenticated requests? Is there an OAuth setup that allows client side requests to OpenAI?
73% of AI startups are just prompt engineering
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Re: 73% of AI startups are just prompt engineering
#52Earlier quoted context omitted.
-1: there's lots of "kingdoms" (openai, anthropic, google, plus open source) - if one king comes for your castle, you can move in minutes.
True, even OpenAI built their castle in nVidia's kingdom. And nVidia built their castle in TSMC's kingdom. And TSMC built their castle in ASML's kingdom.
Re: 73% of AI startups are just prompt engineering
#53Prompt engineering isn't as simple as writing prompts in english. It's still engineering data flow, when data is relevant, systems that the AI can access and search, tools that the AI can use, etc.
Imagine you are a top of the line engenier... Engineering data flow... sure, we all like to use big words.
Re: 73% of AI startups are just prompt engineering
#54Re: 73% of AI startups are just prompt engineering
#55Re: 73% of AI startups are just prompt engineering
#56This makes no sense to me? I don't understand why a company, even if it is using GPT or Claude as their true backend, is going to leave API calls in Javascript that anyone can find. Sure maybe a couple would, but 73% of those tested? Surely your browser is going to talk to their webserver, and yup sure it'll then go off and use Claude etc then return the answer to you, but surely they're not all going to just skin an…
Re: 73% of AI startups are just prompt engineering
#57Where is this guy sitting that he is able to collect all of this data? And why is he able to release it all in a blog post? (my company wouldn't allow me to collect and release customer data like this.)
Is Teja Kusireddy a real person? Or is this maybe just an experiment from some AI company (or other actor) to see how far they can push it? A Google search by that name doesn't find anything not related to the article.
The article should be flagged. Otoh, this should get discussed.
Re: 73% of AI startups are just prompt engineering
#58One of the biggest problems frontier models will face going forward is how many tasks require expertise that cannot be achieved through Internet-scale pre-training. Any reasonably informed person realizes that most AI start-ups looking to solve this are not trying to create their own pre-trained models from scratch (they will almost always lose to the hyperscale models). A pragmatic person realizes that they're not f…
Re: 73% of AI startups are just prompt engineering
#59Where is this guy sitting that he is able to collect all of this data? And why is he able to release it all in a blog post? (my company wouldn't allow me to collect and release customer data like this.)
To be able to call the OpenAI directly from the front end, you'd need to include the OpenAI key, which would be a huge security hole. I don't doubt that many of these companies are just wrappers around the big LLM providers, but they'd be calling the APIs from their backend where nothing should be interceptable. And sure, I believe a few of them are dumb enough to call OpenAI from the frontend, but that would be a minority.
This whole thing smells fishy, and I call BS unless the author provides more details about how he intercepted the calls.
Re: 73% of AI startups are just prompt engineering
#60Prompt engineering isn't as simple as writing prompts in english. It's still engineering data flow, when data is relevant, systems that the AI can access and search, tools that the AI can use, etc.
Is it, though? Apparently the current best practice is just to allow the LLM untethered access to everything and try to control access by preventing prompt injection...
- 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, how many documents to retrieve for analysis, how to chunk documents based on semantics)
- parallelize the search pipeline to decrease wait times
- add moderation
- add a reranker to find best candidates
- add background embedding calculation of user documents
- lots of failure cases to iron out so that the prompt worked for most cases
There's no "just give LLM all the data", it's more complex than that, especially if you want best results and also full control of data (we run all of that using open source models because user data is under NDA)