Embeddings Beyond RAG

Explore embeddings beyond RAG. This talk focuses on using embeddings for efficient customer intention analysis, image search, and text matching, offering practical insights and a glimpse into their broader applications.

Overview

I will talk about why embeddings are such a great thing. They can do so many tasks that we set out a huge LLM to do, but in a much more efficient and cost saving way.

There are tons of use cases for embeddings, and in this talk, I just want to give a simple insight into some use cases of embeddings, beside RAG.

I want to cover (not sure if this is the final list yet):

RAG
Image Search
Image Classifier
Advanced Image Classifier with an added MLP Head
Text Matching across languages
Getting Clear Text Input for Customer Intention Analysis (Main Focus)
And a short example of how you can use that clear text input to improve what you are offering as a company. (Main Focus)

As the 5 Minute Time slot is very narrow, I will likely focus on the Clear Text Input Analysis part, as I think that is quite a nice use case for embedding based, customer facing search.

While I will not show a lot of code in this presentation, coding this yourself is so easy, that anyone could do it without seeing any code. It’s more about the idea and concept for this usecase.

Tech stack