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nilirltoday at 9:58 AM3 repliesview on HN

Maybe I'm old but where exactly are the "dragons"?

How is RAG any different from the search systems we've been building before LLMs? Is it the sudden need for everyone to design a search API and engine that's driven this trend?

If so, I'd like to see more design patterns around existing search problems:

- Correcting or backtracking based on feedback.

- Measuring relevance.

- Comparison with task-based pre-written queries. Does every LLM task need a full blown search engine? Why not a tightly scoped domain API for data retrieval?


Replies

brabeltoday at 10:14 AM

The whole embedding thing which converts “tokens” to vectors, which you then store in a vector database so that you can later query by vector distance, seems to be LLM specific technology, no? As far as I know the vectors look a lot like the weights in a LLM itself which is why the vector search also works with some level of intelligence.

show 4 replies
TudorAndreitoday at 10:03 AM

It's just information retrieval packaged as something new.

show 1 reply
MacketSWEtoday at 10:23 AM

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