On a less sneerious note, I would draw distinctions between:
- Being able to extract value from LLM/GenAI
- LLM/GenAI being able to sustainably produce value (without simple theft, and without cheaper alternatives being available)
And so far i’ve really not been convinced of the latter.
Consider traditional databases which let you search for strings. Vector databases let you search the meaning.
For one client, someone could search for “videos about cats”. With stemming and stop words, that becomes “cat” and the results might be lists of videos about house cats and maybe the unix “cat” command. Tigers, lions, cheetahs? Nope.
Vector database will return tigers/lions/cheetahs because it “knows” they are cats. A much smarter search. I’ve built that for a client.
I realize it’s probably a toy example but specifically for “cats” you could achieve the similar results by running a thesaurus/synonym-set on your stem words. With the added benefit that a client could add custom synonyms, for more domain-specific stuff that the LLM would probably not know, and not reliably learn through in-prompt or with fine-tuning. (Although i’d argue that if i’m looking for cats, I don’t want to also see videos of tigers, or based on the “understanding” of the LLM of what a cat might be)
For the labeling of videos itself, the most valuable labels would be added by humans, and/or full-text search on the transcript of the video if applicable, speech-to-text being more in the realm of traditional ML than in the realm of GenAI.
As a minor quibble your use case of GenAI is not really “Generative” which is the main thing it’s being sold as.
