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Thanks guys very useful

Quite informative!

Special thanks to Mehdi, it is pleasure if we could find a field for cooperation. Kindly let us know how to communicate to you for having discussions on it. Regards

I guess anyone is an AI guru these days 😂

Well done ! I would like to see a comparison in terms quality and scale for classification between a in house trained models vs LLMs !

Great talk! One small point I’d like to mention is that at around 17:55, Angelina “hmm”s five times within the next 15 seconds, which is quite distracting. While this habit might work well in an offline meeting where such sounds signal active listening, in an online setting, it can actually interrupt the flow and impact the quality of the talk-especially when I’m trying to focus on Mehdi’s insights. A little nodding or some sign language with the mic muted would be really appreciated! Anyway, it was a very insightful talk-I’m just nitpicking.

What's new in this compared to Anthropic's post?

Hi there ; I feel a quick rewrite or just a short conversation between llm and user to clarify what the user meant is a much better and cheaper approach.

Thank you for this awesome video 🌱🧝‍♀️

Great job! Keep up the excellent work!

How to chunk it, can you share how to do with local LLM and SQL server database

Another solution to the similarity issues is not to use a cache at all

Dorud Mehdi jan, awesome as always

I think there is one small problem about querying slightly different values. What if user enters values that are slightly different? For example, instead of "Warner Bros", user might enter "warner bros", or "Warner brothers", or "WB". Also, there are different spellings and foreign names for movies, or some movies have "the" such as "The Truman Show" vs. "Trueman Show".

Thank you for the video! An unimportant question: why do you start your prompt with "Please"?!

How would you implement an agent? Do you recommend going with frameworks that provide agents + tools?

I did something similar to this, but connected mine to a MongoDB database. Used Gorilla LLM to get the syntactically correct query to run

Hi Mehdi and Angelina. You are doing great job，really appreciate your content. Can you cover some content on finetuning llm. Always hear voices like finetuing does not have good outcome... or you may need to fine-tune your own model for specific use case like law..... I am wondering how far can fine-tune go these days😅

Excellent explanation thx ! :)

Thanks for sharing great explanation on Agentic AI

Super underrated channel. Just binge watched 5 videos today!

Thank you for clear explanation

Great interview! Really insightful information

I have a huge company policy document that I want to create a knowledge graph for, how do I define labels for that? or is it better to do it without? If yes can you please guide me how to go about it without defining labels?

Will this work if I have JSON data instead of text documents? How to work out contextual embedding for JSON chunks?

Our RAG live course is coming up soon, and as a way of giving back to our amazing community, we're offering you 15% off. Just use this link: maven.com/angelina-yang/mastering-rag-systems-a-hands-on-guide-to-production-ready-ai?promoCode=TwoSetAI We'd love to see you there! 🎉 In the course, you'll have the chance to connect directly with Professor Mehdi (just like I do 😉 in the videos), and you can even ask him your questions 1:1. Bring your real work projects, and during our office hours, we'll help you tackle your day-to-day challenges. This course is for: 01 👇 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 & 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀: For AI engineers/developers looking to master production-ready RAG systems combining search with AI models. 02 👇 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀: Ideal for data scientists seeking to expand into AI by learning hands-on RAG techniques for real-world applications. 03 👇 𝗧𝗲𝗰𝗵 𝗟𝗲𝗮𝗱𝘀 & 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗿𝘀: Perfect for tech leads/product managers wanting to guide teams in building and deploying scalable RAG systems

Our RAG live course is coming up soon, and as a way of giving back to our amazing community, we're offering you 15% off. Just use this link: maven.com/angelina-yang/mastering-rag-systems-a-hands-on-guide-to-production-ready-ai?promoCode=TwoSetAI We'd love to see you there! 🎉 In the course, you'll have the chance to connect directly with Professor Mehdi (just like I do 😉 in the videos), and you can even ask him your questions 1:1. Bring your real work projects, and during our office hours, we'll help you tackle your day-to-day challenges. This course is for: 01 👇 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 & 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀: For AI engineers/developers looking to master production-ready RAG systems combining search with AI models. 02 👇 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀: Ideal for data scientists seeking to expand into AI by learning hands-on RAG techniques for real-world applications. 03 👇 𝗧𝗲𝗰𝗵 𝗟𝗲𝗮𝗱𝘀 & 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗿𝘀: Perfect for tech leads/product managers wanting to guide teams in building and deploying scalable RAG systems

Our RAG live course is coming up soon, and as a way of giving back to our amazing community, we're offering you 15% off. Just use this link: maven.com/angelina-yang/mastering-rag-systems-a-hands-on-guide-to-production-ready-ai?promoCode=TwoSetAI We'd love to see you there! 🎉 In the course, you'll have the chance to connect directly with Professor Mehdi (just like I do 😉 in the videos), and you can even ask him your questions 1:1. Bring your real work projects, and during our office hours, we'll help you tackle your day-to-day challenges. This course is for: 01 👇 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 & 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀: For AI engineers/developers looking to master production-ready RAG systems combining search with AI models. 02 👇 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀: Ideal for data scientists seeking to expand into AI by learning hands-on RAG techniques for real-world applications. 03 👇 𝗧𝗲𝗰𝗵 𝗟𝗲𝗮𝗱𝘀 & 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗿𝘀: Perfect for tech leads/product managers wanting to guide teams in building and deploying scalable RAG systems

Our RAG live course is coming up soon, and as a way of giving back to our amazing community, we're offering you 15% off. Just use this link: maven.com/angelina-yang/mastering-rag-systems-a-hands-on-guide-to-production-ready-ai?promoCode=TwoSetAI We'd love to see you there! 🎉 In the course, you'll have the chance to connect directly with Professor Mehdi (just like I do 😉 in the videos), and you can even ask him your questions 1:1. Bring your real work projects, and during our office hours, we'll help you tackle your day-to-day challenges. This course is for: 01 👇 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 & 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀: For AI engineers/developers looking to master production-ready RAG systems combining search with AI models. 02 👇 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀: Ideal for data scientists seeking to expand into AI by learning hands-on RAG techniques for real-world applications. 03 👇 𝗧𝗲𝗰𝗵 𝗟𝗲𝗮𝗱𝘀 & 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗿𝘀: Perfect for tech leads/product managers wanting to guide teams in building and deploying scalable RAG systems

Our RAG live course is coming up soon, and as a way of giving back to our amazing community, we're offering you 15% off. Just use this link: maven.com/angelina-yang/mastering-rag-systems-a-hands-on-guide-to-production-ready-ai?promoCode=TwoSetAI We'd love to see you there! 🎉 In the course, you'll have the chance to connect directly with Professor Mehdi (just like I do 😉 in the videos), and you can even ask him your questions 1:1. Bring your real work projects, and during our office hours, we'll help you tackle your day-to-day challenges. This course is for: 01 👇 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 & 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀: For AI engineers/developers looking to master production-ready RAG systems combining search with AI models. 02 👇 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀: Ideal for data scientists seeking to expand into AI by learning hands-on RAG techniques for real-world applications. 03 👇 𝗧𝗲𝗰𝗵 𝗟𝗲𝗮𝗱𝘀 & 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗿𝘀: Perfect for tech leads/product managers wanting to guide teams in building and deploying scalable RAG systems

why do we need TD-IDF?

how can i keep the data base in synchronized with my LOB app say ERP

excellent format! and great topic.

What if the length of the 'entire doc' exceeds the 'token-limit' of the Anthropic LLM ?

Yes ! We are obtaining ggood results from contextual retrieval before and now !

Great content! It’s nice to see your channel growing too 😁

Amazing video! A followup question: regarding the judge who decide 0 or 1, what if the judge is incorrect? Any methods about how can we make this role robust enough? Thx!

is the .ipynb files from this video sourced somewhere for use?

It could be very useful if you might also provide a code project to show how to build Graph RAGs

Url for the code is on github. search for "twosetai" on github.

Awesome video as usual. Can't wait to do the course.

Excellent tutorial , and in very informative and simple language, can you please share the code with us.

can i get GitHub link?

My only question on this is about the Data Sceurity. Exposing database directly to LLM might be risky. As we have seen many times that certain prompts can some time leak crucial data. So LLM having all the access to the DB without Row Level Security or in this case, any kind of security will be a big big risk to the Organizations

beautiful

👍

Thanks for the valuable info

Interesting video, but I have issues with the paper. (1) Optimizing each step and assuming that will give the global optimum seems a bit naïve. (2) I'm surprised by the exclusion of chunking strategies like LangChain's recursive chunker. It seems hard to see how a simplistic token count based chunking could ever be better than one that takes into account paragraphs etc (and it's probably faster than sentence level chunking).

could you please share the code for this

The keypoint in this demo is the pertained model using gpt-3.5 and must be online.