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Comment by nilirl | Hacker News Reader

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?

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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.

Vector embeddings predate LLMs. They have been used as far back as the early 2000s. They are a general machine learning technique, rather than LLM specific

Unfortunately LLMs made vector search more popular so it seems like something LLM specific.

What makes it worse, a lot of people in the thread equate vector search with RAG, whereas RAG is the name for anything that model can query so a user doesn't have to copy/paste feed it to the model manually like access to text files is RAG.

Sure and that's a new technique for indexing and querying.

Where's the new design tension? Indexes always had to be monitored for freshness and queries have always needed cleaning or parsing.

right, it is the foundation of machine learning.

not really, vectorising text/books is old school ML by this point.

at least to me that seems the same as https://en.wikipedia.org/wiki/Word2vec for e.g.

Well... Everything new is old "A vector space model for automatic indexing" 1975 - https://dl.acm.org/doi/10.1145/361219.361220

I wonder who was doing doing semantic search in the last century!

"The future is already here—It's just not very evenly distributed..."

Sure, the idea of making a vector embedding for words, sentences, documents etc. is old, but the meat is in how you construct this embedding. I think embeddings have gotten quite a bit better since word2vec.

It's just information retrieval packaged as something new.

It's just information retrieval through a new NN based technology that allows to map concepts and ideas as the compression of long text into points in a multidimensional space that manages to compress even more dimensions than the given ones, through non-transparent engines that give different mappings and results, and still (the information retrieval) requires many more clever tricks than the simple idea of vector distance ordering because things do not quite work as they should.

Let's say it's just "computation packaged as something new". "Trivial things".

And you can't fundraise on some old "information retrieval".

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