RAG, semantic search, embedding, vector... Find out what the terms used with Generative AI mean!
ฝัง
- เผยแพร่เมื่อ 2 มิ.ย. 2024
- In this video I explore HOW generative AI works with your data and why terms like retrieval augmented generation (RAG), semantic index, semantic search, vectors and embeddings are so important.
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▬▬▬▬▬▬ C H A P T E R S ⏰ ▬▬▬▬▬▬
00:00 - Introduction
00:29 - My typical day and need for information
03:58 - RAG
05:16 - LLM refresher
08:16 - Orchestrators and information to LLMs
12:56 - Semantic index, search, vector, embeddings?
15:26 - Embedding models and creating vector
21:12 - 2 dimensions
23:58 - Semantic search and nearest neighbor
26:31 - Why embeddings and semantic search are so important
27:36 - Summary and close
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Giving large language models access to your data makes them so much more useful but how does all this work and what does it have to do with vectors. In this video we find out! Please make sure to read the description for the chapters and key information about this video and others.
⚠ P L E A S E N O T E ⚠
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Thanks for watching!
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By far the best and most complete explanation on those fundamental topics that I've seen. Once you understand these foundational concepts, the whole stack becomes "easy" to visualize and makes conversations around how to apply these technologies to customer challenges easier as a result.
Thank you and 100% agree on the importance of understanding these and how everything falls into place once you do.
@@NTFAQGuy I may borrow from your presentation the next time I'm talking to a customer at the EBC 😃
ROFL.
Great explanation and to the point. Thanks!!
You're welcome!
Thanks very much for the video.
My pleasure!
Awesome video, i have no doubt a lot of research and time went into being able to explain such a complex topic in a very understandable format
As always, so much information packed in one video! Thanks for sharing all your hours of research in this 29 mins video.
My pleasure!
Awesome video, very important concepts for any LLM work! I was just wondering if you have any details about the difference between Cognitive Search Semantic retrieval versus Embedding. It sounds like they kind of have the same goal and work similarly, so I'm wondering what we should use in any specific scenario.
Well done John. Very well explained. Have a great thanksgiving.
Thanks, you too!
Really informative and well visualised. Thanks for another great share!
My pleasure!
Thank you it was a nice explanation
Glad it was helpful!
Great explanation of the terminology. Very helpful
Glad it was helpful!
Amazing John, thanks 🤩
Glad you enjoyed it
The best teacher forever ! Thks for this share ❤
My pleasure!
Thanks John!
John, you really are one of the best teachers out there.
That is very kind, thank you.
That was awesome. Thank you
Very welcome
As always, great content! You mention around 8:09 that you did a whole video around how large language models work. I'm definitely interested in that, is it the one about ChatGPT?
Yes
Awesomeness 😊😊
Thanks 🤗
My... brain... hurts... but hopefully I'm a little less dumb than I was yesterday. Thanks John!
yeah it took me a while. little bit of learning at a time ;-)
Was there supposed a link about previous video about LLM as noted in a video?
It was linked as a card in the video but added it to description as well.
too good most optimal in the current situation. The other thing i liked in the video is the picture of the cat :-)
Thank you so much 😀
Your first boss looked like a cat
Where do you get your cool shirts from? 😎
just random places. No single place really.