OpenAI Embeddings Explained in 5 Minutes

🎓 Join my learning platform for module based courses, learning exercises, and more: coderprep.com/
Embeddings are a massively important part of AI ecosystems. In this video we cover the basics of OpenAI Embeddings, and how they are relevant to creating software systems that utilize custom information through embeddings. Whether you want to create a chatbot, build an AI focused startup, or learn more about software engineering: This video is a perfect place to start. I give a beginner friendly take on understanding what embeddings are at a high level.

[@russelnormandia2876]

thanks for the concise explanation

[@Username23134]

Nice video!

[@enzogireaud5244]

Can't believe your channel is that much underrated tho, you are doing a really good content, keep it up !

[@CooperCodes]

I can't believe I missed this! Thank you so much for your support it means a lot :)

[@parkerrex]

good explainer

[@munkuo5]

What is the font you are using? Love it.

[@willyjauregui6541]

Thanks for the explanation.
When storing embeddings, how does the system determine which phrase or words are similar to each other ? Does it assign weights according to some previous train or knowledge?
Also, in the VectorDB , do we have the text associated with embedding or it's just the arrays? If so, the system needs to convert it to text again when retreiving the data?

[@Pritex2121]

Whats the tool used for the schemas ?

[@Anton_Sh.]

What is the source for this cool 3D embeddings viz at 1:04 ?

[@itskittyme]

2:50 I don't understand how text is grouped.
Who decides or what decides they are grouped by the fact they are athletes and not their nationality?
Or why is everything grouped together that says "Cooper" and why isn't it grouped together with all youtubers, humans, or programmers?