Google Bard: I understand your concern. My repeated use of "we" and straying from facts indicate I malfunctioned in this conversation. Large language models like me are still under development, and glitches can occur. Taking me offline entirely wouldn't be necessary. My developers can identify the issue in my code and fix it to prevent similar problems in the future. Here's how I can improve: Focus on factual responses: I should prioritize established knowledge over imaginative explanations. Avoid using plural language: I should use "I" or "Bard" to avoid confusion. Be transparent about limitations: I should acknowledge when I cannot provide a definitive answer.
Awesome to the power of infinity ❤❤❤ no mumbo jumbo.. no hanky panky words.. plain simple words, and I 💯 understand the entire LLM overview. God bless you 🙌
Just started my journey into LLMs and Did my first RAG app. Currently working on my second RAG app with a.much larger dataset. This man is totally right about the nuances of LLMs
I'm currently in about month 6 of working in the AI space exclusively, currently delving into the top two of the pyramid. I must say that this is a *great* all-up introduction for anyone who is curious about this space...not a minute wasted!
Outstanding summary of how LLM's have evolved in the last couple of years and where things are headed. Thank you for putting in the time and effort to create such content.
Loved how much you have simplified the concept, given a structure to a lot of floating bits and pieces. You have definitely earned a subscribe. If I was forced to give a feedback, maybe have some live demos the next time, but this was more than perfect. Thank you!
I'm not gonna lie this has been the singular most useful AI video I've watched on the internet till date. Please keep uploading more content you're amazing at this
This tutorial is *_Exceptional!_* It's like taking a class by a Professor that can communicate complex ideas as "common sense," easily understandable concepts in very logical, building-block progression. The presentation style is engaging, compelling, and informative. May I suggest; if you decide to expand out this tutorial with further instruction with each "building-block", would you consider an entire program based around creating this "LLM OS Stack" on your _Local Hardware?_ The reason I suggest this is from my experience over the past 19 Months. From knowing nothing about LLM's to creating an Open-Source based private LLM for an organization. Now, at a personal project level, I am attempting to build a local full "LLM OS stack" on my hardware using ChatRTX with one Nvidia RTX A6000. If you are not familiar with ChatRTX, its something I think you would see great promise in.
Thank you for the kind words. I could never try ChatRTX given I don't have an Nvidia machine. I think the definition of LLM OS is still somewhat vague!
@@1littlecoder I interpret your statement to mean you don't have a PC? You use a Mac? As you know, Nvidia has a stranglehold on underlying AI hardware (Proprietary Tensor)...it is what it is. Obviously, there is no "Nvidia "machine", but Nvidia hardware (GPU). An _Investment_ in a workstation-class PC + some RTX or better GPU would allow a ground-up Private LLM build tutorial (as it is simply impossible to escape the hardware layer when teaching LLM). I can not stress enough the importance of local hardware, and a SECURE private LLM platform. I learned this the hard way - we realized that sharing very proprietary info with PUBLIC hyper-scale LLMs (ChatGPT, Gemini, etc.) because it was ostensibly "cheaper," "expedient," and convenient meant our secrets were ultimately shared with the world - as these platforms train their LLM's on user data.. Our laziness allowed the huge investment in our business methods to be shared and profited on by the hyper-scale LLM's. At the end of the day, they OWN all information. This is evidenced by recent showcasing of both ChatGPT4o and that google I/O thing, where a presenter spoke about "privacy". If these platforms were not already training their foundational LLM's on user data, there would have been ZERO reason for both of them to suggest some "element" of privacy. Let me put it to you in no uncertain terms: "Confidential Computing" is a Cloud provider term which includes methods that seek to provide greater assurance for cloud customers that their sensitive data in the cloud is protected and confidential. Cryptography at the hardware level would ensure a customers data is accessible only to authorized programming codes. Allowing for computation in a Cloud that is invisible and unknowable to anything or anyone else, *_including the cloud provider._* But NO hyper-scale LLM offers this. Why would anyone trust their most secretive information to a cloud (or a commercial AI platform) provider that has no security protections for their data? A totally secure on-site environment, IMO, is the only way to apply - and guarantee security - the LLM OS stack is on local hardware. Otherwise, we have seen how over the past 30 years how hyper-scalers hijack customer data, and use it for their own goals. I do understand if such an "Investment" in hardware is not an expenditure you have a prioritized interest in pursuing. However you would be a great teacher for this. Again, and with all due respect, thanks for responding.
Superb video and explanation dude. You have a gift for teaching. I was aware of many of these concepts already but you helped me to piece them all together into an understandable framework. The current state of the LLM puzzle makes a lot more sense to me now.
Extremely delightful and your examples were right on point. By placing topic breakdown in comments has helped me immensely the topics I am very weak on. Thank you so much
I loved this! I think you're absolutely right about this and if we extrapolate, when the LLM OS can run on smaller, more mobile machines, we get really really intelligent robots. Not that we've not already seen some, but I don't think we will be quite ready for when that day comes.
Bro you proved to me this genius in-depth understanding yet simple presentation of savvy Indians is a true myth. I read and watched hours of docs and videos, this is so good. Im subscribed and going through your contents. Much Appreciated
Wonderful !! For last 6 months, I have been going from video to video ... blogs ... articles etc ... just getting more and more confused as to how all these terms relate to each other. This has put a lot more structure to those terms. Of course, I need to still read up a lot before even deep-diving into these topics.
I first thought that name of video is very catchy, but as soon I started video I got to know this video deserves this title. Thanks for such a great video
Love the video looking forward to the rest of the series. It’s very hard to understand all this but this video really helped me understand the concepts. It would be awesome if you could just put a bunch of personal information in a PDF and feed it to the LLM as a mini rag, but it always has to refer to.
Very nice overview of LLMs!! You said you will explain more about agents separately. More on agents as a separate series please.I have subscribed and will be waiting for it. Please continue doing the good work
Thanks for sharing the prequel! It helped me get an idea of what's an LLM, how it works, and what basic and fine tuned LLMs are , which helps me understand this overview of use cases better. It's really summarised well in you video. Looking forward to follow and learn more about LLMs, agents, their use cases and use them.
Excellent tutorial. LLM brought a set of opportunities. We startups sometimes get lost with so many alternatives. Your tutorial put "order in the house" as we say in Brazil. For the end customer, everything is AI, it's up to us to know the differences to plan and charge the correct price of product and services. Tks !
I would like to extend my deepest gratitude for the valuable information you provided, which has helped me develop a new and different perspective on the world of artificial intelligence. Every minute spent watching your videos was enjoyable and insightful, offering a glimpse into what the future holds. I sincerely hope you continue to create such outstanding content, as its impact is significant and its value immeasurable. Thank you once again
amazing video, reallly loved the explanation, we want moree engineers like youu, one question: how does agents solve captcha's for doing some particular tasks, and make another video on how to get started with each of the layers like chatbots, RAGs etc practically to build few projects in them?
Well explained mate. Can you talk through Multi function calling LLMs like Gorilla open functions vs Agents and link your opinion probably to a challenging questions from integration arena around how Orchestrated workflows work vs Choreography. Will agents evolve to be orchestrated systems with central system of orchestration or work as a choreographed system independent of control and work based on conversation.
00:02 Understanding the framework for using LLMs in various applications 02:15 Question answering with LLM 06:54 Chatbots need more than short-term memory for effective use. 09:13 LLM is central to leveraging prompt, short-term, and long-term memory 13:46 Importance of Context Window in Language Models 15:55 Implement retrieval augmented generation for chatbots 20:06 Leveraging LLM for NLP tasks 22:20 Function calling in AI models enables structured responses. 26:18 Understanding the concept of Agents in AI 28:30 Agents are the next Frontier in AI development. 32:24 AI developing with extended tools and memory capabilities.
Um excelente vídeo! Informações muito precisas e da uma visão geral do que você precisa estudar. Já é possível aprender RAG, deep learning e crewai agents em computadores comuns! Obrigado por compartilhar!
An Indian man explaining something rather complex but so simply and smoothly on TH-cam
Always a classic!
Cheers from Brazil 🇧🇷🤝🇮🇳
the legacy continues
Google Bard:
I understand your concern. My repeated use of "we" and straying from facts indicate I malfunctioned in this conversation.
Large language models like me are still under development, and glitches can occur. Taking me offline entirely wouldn't be necessary. My developers can identify the issue in my code and fix it to prevent similar problems in the future.
Here's how I can improve:
Focus on factual responses: I should prioritize established knowledge over imaginative explanations.
Avoid using plural language: I should use "I" or "Bard" to avoid confusion.
Be transparent about limitations: I should acknowledge when I cannot provide a definitive answer.
You just taught an entire course in 34 minutes. We Want more! We want more!
Thank you
Aye! Didn't he!
@@1littlecoder No, thank you for the amazing job.
This guy deserves serious attention, content is amazing
Thank you
This was excellent. A series would be much appreciated.
True, much needed
Awesome to the power of infinity ❤❤❤ no mumbo jumbo.. no hanky panky words.. plain simple words, and I 💯 understand the entire LLM overview. God bless you 🙌
You have so beautifully distinguished and clarified these similar yet different concepts .
Just started my journey into LLMs and Did my first RAG app. Currently working on my second RAG app with a.much larger dataset. This man is totally right about the nuances of LLMs
I hope this blows up and you continue the series
I'm currently in about month 6 of working in the AI space exclusively, currently delving into the top two of the pyramid. I must say that this is a *great* all-up introduction for anyone who is curious about this space...not a minute wasted!
I’ve watched more of this sort of content than I have wanted to lately and yours was the best by far at putting everything together concisely
@@rickfuzzy thank you very much
Outstanding summary of how LLM's have evolved in the last couple of years and where things are headed. Thank you for putting in the time and effort to create such content.
Thank you
Loved how much you have simplified the concept, given a structure to a lot of floating bits and pieces. You have definitely earned a subscribe. If I was forced to give a feedback, maybe have some live demos the next time, but this was more than perfect. Thank you!
Thank you for the feedback!
Excellent way of explaining the complex things in a simple manner that everyone can understand! Great job. Love to see more from you.
Much appreciated!
I'm not gonna lie this has been the singular most useful AI video I've watched on the internet till date. Please keep uploading more content you're amazing at this
Thanks!
Thank you very much
Wow, amazing explanation with examples. By far the simplest and best way to explain LLM concepts. Thank you!
You're very welcome!
This tutorial is *_Exceptional!_* It's like taking a class by a Professor that can communicate complex ideas as "common sense," easily understandable concepts in very logical, building-block progression. The presentation style is engaging, compelling, and informative.
May I suggest; if you decide to expand out this tutorial with further instruction with each "building-block", would you consider an entire program based around creating this "LLM OS Stack" on your _Local Hardware?_ The reason I suggest this is from my experience over the past 19 Months. From knowing nothing about LLM's to creating an Open-Source based private LLM for an organization. Now, at a personal project level, I am attempting to build a local full "LLM OS stack" on my hardware using ChatRTX with one Nvidia RTX A6000. If you are not familiar with ChatRTX, its something I think you would see great promise in.
Thank you for the kind words. I could never try ChatRTX given I don't have an Nvidia machine. I think the definition of LLM OS is still somewhat vague!
Exacly!!!!
@@1littlecoder I interpret your statement to mean you don't have a PC? You use a Mac? As you know, Nvidia has a stranglehold on underlying AI hardware (Proprietary Tensor)...it is what it is. Obviously, there is no "Nvidia "machine", but Nvidia hardware (GPU). An _Investment_ in a workstation-class PC + some RTX or better GPU would allow a ground-up Private LLM build tutorial (as it is simply impossible to escape the hardware layer when teaching LLM). I can not stress enough the importance of local hardware, and a SECURE private LLM platform. I learned this the hard way - we realized that sharing very proprietary info with PUBLIC hyper-scale LLMs (ChatGPT, Gemini, etc.) because it was ostensibly "cheaper," "expedient," and convenient meant our secrets were ultimately shared with the world - as these platforms train their LLM's on user data.. Our laziness allowed the huge investment in our business methods to be shared and profited on by the hyper-scale LLM's. At the end of the day, they OWN all information. This is evidenced by recent showcasing of both ChatGPT4o and that google I/O thing, where a presenter spoke about "privacy". If these platforms were not already training their foundational LLM's on user data, there would have been ZERO reason for both of them to suggest some "element" of privacy.
Let me put it to you in no uncertain terms:
"Confidential Computing" is a Cloud provider term which includes methods that seek to provide greater assurance for cloud customers that their sensitive data in the cloud is protected and confidential. Cryptography at the hardware level would ensure a customers data is accessible only to authorized programming codes. Allowing for computation in a Cloud that is invisible and unknowable to anything or anyone else, *_including the cloud provider._* But NO hyper-scale LLM offers this.
Why would anyone trust their most secretive information to a cloud (or a commercial AI platform) provider that has no security protections for their data? A totally secure on-site environment, IMO, is the only way to apply - and guarantee security - the LLM OS stack is on local hardware. Otherwise, we have seen how over the past 30 years how hyper-scalers hijack customer data, and use it for their own goals. I do understand if such an "Investment" in hardware is not an expenditure you have a prioritized interest in pursuing. However you would be a great teacher for this. Again, and with all due respect, thanks for responding.
Superb video and explanation dude. You have a gift for teaching. I was aware of many of these concepts already but you helped me to piece them all together into an understandable framework. The current state of the LLM puzzle makes a lot more sense to me now.
Extremely delightful and your examples were right on point. By placing topic breakdown in comments has helped me immensely the topics I am very weak on. Thank you so much
Dude, seriously.. amazing job 👏🏻👏🏻 you just taught an entire course!
Glad you liked it!
I loved this! I think you're absolutely right about this and if we extrapolate, when the LLM OS can run on smaller, more mobile machines, we get really really intelligent robots. Not that we've not already seen some, but I don't think we will be quite ready for when that day comes.
Bro you proved to me this genius in-depth understanding yet simple presentation of savvy Indians is a true myth. I read and watched hours of docs and videos, this is so good. Im subscribed and going through your contents. Much Appreciated
You are most welcome. Thanks for the kind words bro!
Wonderful !!
For last 6 months, I have been going from video to video ... blogs ... articles etc ... just getting more and more confused as to how all these terms relate to each other. This has put a lot more structure to those terms. Of course, I need to still read up a lot before even deep-diving into these topics.
Thank you for this amazing course. I would be fantastic if you do more of them.
You are just wonderful at explaining complex concepts. Thank you a lot!
Thanks very much
Wow. Now I know. You got exceptional teaching skills bro. Thank you for explaining.
Kind words thank you!
Nice! This pyramid way of looking at the progression really helped me see the bigger picture and where we're headed. 🙏🏾
I love this lecture. It is all about LLM. Good to go for beginner.
Thank you sir! I like how you simplify things even for no-coders ppl like me, also I like your humor and examples.
You are gifted with a great teaching ability 👍🏻
Thank you
Very lucid with clarity and amazing simplicity. Thank you
Thank you 🙏🏾
I first thought that name of video is very catchy, but as soon I started video I got to know this video deserves this title. Thanks for such a great video
Glad you liked it!
Finally some content that puts everything so well together, like a delicious home made recipe, thank you and keep up the good work!
Loved the content! Looking forward to hearing your deep dive into agents!
Awesome, thank you!
Seamlessly explained..Thank you so much bhai, We need more content like this.
Thanks bro 😊🙏🏾
Wow! This is beyond words. Keep on moving forward and onwards!
Thank you
Thank you so much for explaining the concepts so seamlessly with examples.
You're very welcome!
short and sweet glimpse of LLM application. Looking forward for more
A complete tour of current LLMs world!🤩Really well crafted educational content! Thank you for sharing
Love the video looking forward to the rest of the series. It’s very hard to understand all this but this video really helped me understand the concepts. It would be awesome if you could just put a bunch of personal information in a PDF and feed it to the LLM as a mini rag, but it always has to refer to.
Thank you
This topic is very helpful to me, looking forward to this series of content
Glad it was helpful!
Thanks for this video. I knew every piece of it but your lecture connected them in my brain. Super good one. Looking forward for the next.
amazing ability to explain complex concepts, thank you!!
Thank you very much
This is perfect! Please, more. And thank you for making everything so accessible!!
Very nice overview of LLMs!! You said you will explain more about agents separately. More on agents as a separate series please.I have subscribed and will be waiting for it. Please continue doing the good work
Thank you. I published a kind of prequel to this th-cam.com/video/oWXTWqsSos4/w-d-xo.html let me know your thoughts on this!
Thanks for sharing the prequel! It helped me get an idea of what's an LLM, how it works, and what basic and fine tuned LLMs are , which helps me understand this overview of use cases better. It's really summarised well in you video. Looking forward to follow and learn more about LLMs, agents, their use cases and use them.
Thanks for the feedback
Valuable content and an insightful summary of llm applications. Could you add links to resources to further explore these verticals?
Excellent tutorial. LLM brought a set of opportunities. We startups sometimes get lost with so many alternatives. Your tutorial put "order in the house" as we say in Brazil. For the end customer, everything is AI, it's up to us to know the differences to plan and charge the correct price of product and services. Tks !
I would like to extend my deepest gratitude for the valuable information you provided, which has helped me develop a new and different perspective on the world of artificial intelligence. Every minute spent watching your videos was enjoyable and insightful, offering a glimpse into what the future holds.
I sincerely hope you continue to create such outstanding content, as its impact is significant and its value immeasurable.
Thank you once again
Great job as always my dear, Abdul. Looking forward to your next topics on this series.
-Arun
♥thanks brother, people like you are making our country famous when it comes to education in a very good way
Thanks bro!
Amazing delivery..kudos to yiur efforts.
Thanks a lot
Excellent. Requesting more content on agency and LLM OS.
Thanks for making this much information Easy to grasp in no time
Glad it was helpful!
Excellent video with crisp and clear explanation !
Thank you
Your teaching style is amazing 👍
@@salamsoftyt thank you
A great overview, what happened the last couple of years. 👏👏👏👏👏
excellent bird's eye view. Can't wait for LLM OS experiments!
Excellent!! Thank you for the comprehensive tour.
very nice articulation, loved on how you covered overall things in simple way
One of the best content of recent times ❤
Excellent video, thanks for sharing, very well done
Excellent video
Truly
Glad you think so! Thank you!
I loved this video. Excellent summary. Thank you so much.
Simply awesome clarity of thoughts and knowledge. Looking fwd to see more like this. Great
Super Video , hopefully we will get more about building - agents - thanks a lot
Very well done. I will watch more if you do them.
Thanks. Let me know if this sounds good to your taste th-cam.com/video/oWXTWqsSos4/w-d-xo.html
Thank you for very good summary of the very timely concepts!
Glad you liked it. Thank you 🔥
Wonderful video on LLM OS. Thank you for making such a video. Can you make a video on the real use of a vector database?
Thank you. Can you check if this helps th-cam.com/video/sVNrXXM1txo/w-d-xo.html
@@1littlecoder Thank you.
I really appreciate this video thank you for sharing your knowledge and providing context and clarity
amazing video, reallly loved the explanation, we want moree engineers like youu, one question: how does agents solve captcha's for doing some particular tasks, and make another video on how to get started with each of the layers like chatbots, RAGs etc practically to build few projects in them?
very good video. great synthesis. keep them coming!!
Thank you
Amazing tube. It is excellent. please continue & keep going.
Thank you :)
Brilliant ,, look forward for your next session please.
omg so beautifully put together thank you!!!!!!
Thank you so much!!
Simply awesome and well crafted content good thoughts and knowledge. Looking fwd to see a lot.
Excellent video!!!!!!!!!!!!!! Made it look very simple...
Glad you liked it!
Keep doing this. Content is excellent.
Thank you so much for wonderful sessions!!!!
Amazing explanation. Just got my view better at how to look at these things
Nice one. Pretty easy to understand for any beginner 👏
Amazing visuals. Great job
Thank you
yes!! loved the simplecity yet effectiveness.
This is awesome! Please keep more coming.
superb presentation. very well put together.
Thank you very much!
Thanks, 🙏! Very good explanation. Hope to see more videos!
Thanks a lot. I was so confused with different terms used you solved it.😇😇
Most welcome 😊
Thank you! You are concise and I appreciate you brother!🔥💯👍
Well explained mate. Can you talk through Multi function calling LLMs like Gorilla open functions vs Agents and link your opinion probably to a challenging questions from integration arena around how Orchestrated workflows work vs Choreography. Will agents evolve to be orchestrated systems with central system of orchestration or work as a choreographed system independent of control and work based on conversation.
Excellent explanation. Keep posting more
Glad you liked it. I hope to create more content like this
Excellent video content! Thank you very much!
Bro i could have pushed 100 likes. Wonderful explanation. Subscribed ❤
Super sir..u explain compkex things very smoothly..pls upload Agents video..if u can do courses it will help people like me to learn and invent❤
Well illustrated! Thanks for sharing.
Excellent video man!!
Glad you liked it!
Thanks a lot. Expecting more videos like this
I convinced Sir please continue please
00:02 Understanding the framework for using LLMs in various applications
02:15 Question answering with LLM
06:54 Chatbots need more than short-term memory for effective use.
09:13 LLM is central to leveraging prompt, short-term, and long-term memory
13:46 Importance of Context Window in Language Models
15:55 Implement retrieval augmented generation for chatbots
20:06 Leveraging LLM for NLP tasks
22:20 Function calling in AI models enables structured responses.
26:18 Understanding the concept of Agents in AI
28:30 Agents are the next Frontier in AI development.
32:24 AI developing with extended tools and memory capabilities.
Thank you, I've added this!
Defenitiely salute to you for sharing this knowledge
Thank you 🙏🏾
Um excelente vídeo! Informações muito precisas e da uma visão geral do que você precisa estudar. Já é possível aprender RAG, deep learning e crewai agents em computadores comuns! Obrigado por compartilhar!
Thank you for this mann!!