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Consensus: Use AI to find insights in research papers

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Re: Consensus: Use AI to find insights in research papers

#6
post #5

Is this another SaaS making API calls to OpenAI or is it more than that? If so, how?

Embed articles and throw the results in a vector database.

Throw up a search result that just uses cosine similarity on the vector search with questionable metrics and no explanations on how things are calculated.

Charge yearly because you know people will churn after a month or two.

Profit

- Every "AI startup" in the last 2 months

Re: Consensus: Use AI to find insights in research papers

#8
post #6
post #5

Is this another SaaS making API calls to OpenAI or is it more than that? If so, how?

Embed articles and throw the results in a vector database. Throw up a search result that just uses cosine similarity on the vector search with questionable metrics and no explanations on how things are calculated. Charge yearly because you know people will churn after a month or two. Profit - Every "AI startup" in the last 2 months

I'll play DA here - there's quite a bit of engineering surrounding these apps that can appear hidden to folks from the outside looking in. Various levels of prompt engineering and in-context learning might be necessary to get optimal results, and this could mean significantly more complexity at the application level.

Re: Consensus: Use AI to find insights in research papers

#9
post #8
post #6

Earlier quoted context omitted.

Embed articles and throw the results in a vector database. Throw up a search result that just uses cosine similarity on the vector search with questionable metrics and no explanations on how things are calculated. Charge yearly because you know people will churn after a month or two. Profit - Every "AI startup" in the last 2 months

I'll play DA here - there's quite a bit of engineering surrounding these apps that can appear hidden to folks from the outside looking in. Various levels of prompt engineering and in-context learning might be necessary to get optimal results, and this could mean significantly more complexity at the application level.

Every time I hear or read "prompt engineering", I can't help but cringe a bit. I'm not sure why, but it's the same reaction I would have if I heard someone say "Google search query engineering".

Comparing to google search, there definitely is skill involved in knowing how to google things well. We're all accustomed to googling things many times per day so I think a lot of people forget that being able to google things and get the results you want is a skill that has to be learned.

But I would never refer to being "good at writing google search queries" as any kind of engineering. But is becoming good at searching google any less difficult than getting good at writing LLM prompts?

I'd love to hear the other side of the argument. How difficult is it to become good at "prompt engineering"? Why do we even call it "prompt engineering" instead of just "writing effective prompts"?

Edit: I think the main gripe I have with the term "prompt engineering" is it makes the skill of writing prompts sound a lot harder than it actually is. Maybe I'm underestimating how difficult it is to learn how to write good prompts?

Re: Consensus: Use AI to find insights in research papers

#10
I felt really excited clicking on some of the suggested prompts, but that excitement quickly fell apart when trying to generate a summary for what I consider to be a fairly simple custom search:

impact of airbnb listings on house prices → can't summarise, need to post it as a question.

how do airbnb listings impact house prices? → can summarise, can't create a concensus, must use a yes/no question.

do more airbnb listings increase house prices? → can summarise, can't create a concensus because there's not enough relevant search results. But the maximum is 20 (according to the info icon) and it found 11 highly relevant articles, so I really don't understand how there isn't enough relevant search results.

And I gave up and deleted my account.

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