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 ⚠ 🔎 If you are looking for content on a particular topic search the channel. If I have something it will be there! 🕰 I don't discuss future content nor take requests for future content so please don't ask 😇 🤔 Due to the channel growth and number of people wanting help I no longer can answer or even read questions and they will just stay in the moderation queue never to be seen so please post questions to other sites like Reddit, Microsoft Community Hub etc. 👂 Translate the captions to your native language via the auto-translate feature in settings! th-cam.com/video/v5b53-PgEmI/w-d-xo.html for a demo of using this feature. Thanks for watching! 🤙
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.
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.
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?
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 ⚠
🔎 If you are looking for content on a particular topic search the channel. If I have something it will be there!
🕰 I don't discuss future content nor take requests for future content so please don't ask 😇
🤔 Due to the channel growth and number of people wanting help I no longer can answer or even read questions and they will just stay in the moderation queue never to be seen so please post questions to other sites like Reddit, Microsoft Community Hub etc.
👂 Translate the captions to your native language via the auto-translate feature in settings! th-cam.com/video/v5b53-PgEmI/w-d-xo.html for a demo of using this feature.
Thanks for watching!
🤙
30 mins of this video is worth more than days of official training that microsoft provides. AMAZING!!
I love this. It's very simplified and straight to the point.
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.
The best explanation of these concepts I've seen, great as usual John!
As always, so much information packed in one video! Thanks for sharing all your hours of research in this 29 mins video.
My pleasure!
Great video. Clear, concise, and easy to understand explanations. Thanks!
Glad it was helpful!
Glad I watched the entire video! Exactly what I was looking for especially with the Azure context lens applied. Awesome! Keep up the good work
Glad it was helpful!
Great and comprehensive - as per usual! Top quality JS. Cheers
Many thanks!
John, you really are one of the best teachers out there.
That is very kind, thank you.
Very useful info John! Very comprehensive explanation on how RAG works.
Really Great Explaination, Thanks John :)
Great explanation. Thank you!
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
Great explanation of the terminology. Very helpful
Glad it was helpful!
Well done John. Very well explained. Have a great thanksgiving.
Thanks, you too!
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.
Thanks very much for the video.
My pleasure!
The best teacher forever ! Thks for this share ❤
My pleasure!
Really informative and well visualised. Thanks for another great share!
My pleasure!
Great explanation and to the point. Thanks!!
You're welcome!
Thanks John!
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
Thank you it was a nice explanation
Glad it was helpful!
Amazing John, thanks 🤩
Glad you enjoyed it
you are always exceptional!
Impressive Garfield drawing skills, now I need to replace my short term goal of passing the AI-900 with learning to draw kittens.
That was awesome. Thank you
Very welcome
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
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.
That should be the copilot slogan. "Copilot, for people who are too important to bother to do it themselves". 😂
Where do you get your cool shirts from? 😎
just random places. No single place really.