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
good question. this is one of the reasons why RAG is relevant and important. Check this post: open.substack.com/pub/mlnotes/p/why-use-rag-in-the-era-of-long-context?r=164sm1&
You don't necessarily need tf-idf. It's just a better approach to have two types of search mechanism. 1. semantic search and 2. keyword search. for keyword search tf-idf or BM25 is a natural choice.
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
Yes ! We are obtaining ggood results from contextual retrieval before and now !
Great content! It’s nice to see your channel growing too 😁
Thank you for your support! ☺
What if the length of the 'entire doc' exceeds the 'token-limit' of the Anthropic LLM ?
good question. this is one of the reasons why RAG is relevant and important. Check this post: open.substack.com/pub/mlnotes/p/why-use-rag-in-the-era-of-long-context?r=164sm1&
Thank U for the relevant Article / Post !
excellent format! and great topic.
why do we need TD-IDF?
You don't necessarily need tf-idf. It's just a better approach to have two types of search mechanism. 1. semantic search and 2. keyword search. for keyword search tf-idf or BM25 is a natural choice.
Will this work if I have JSON data instead of text documents? How to work out contextual embedding for JSON chunks?
It depends on the json data. What is the use case for json? what kind of json data do you have?