What is Retrieval Augmented Generation (RAG) and JinaAI?

Retrieval Augmented Generation (RAG) is one of the big AI patterns you must know for 2024, in this tutorial i break down the RAG pattern, What the Jina AI embeddings model isand why JinaAI is a game changer for LLM's such as GPT, Llama and Mistral.

In the video, chris breaks down what the issues with LLM's such as GPT, Mixtral 7B and Llama-2 are and how the RAG pattern helps solves the problems of hallucinations, extending data.

Chris also shows you in detail on how the RAG pattern exacty works under the hood, so you can truly understand what's going on

He also talks about how JinaAI is different, how it works, how compares to openai ada embeddings model and how Jina AI will kick off the next model trend for 2024.

[jonb]

Great video Chris. Even I could understand your explanation!

[mohamedghazal]

Thank you! very simple, precise, yet very informative!

[JAnders-oysv]

Another clear and informative video, thank you! I agree, I think RAG will be huge in 2024. One thing I would like to know, is it possible to have the LLM list or identify the chunk or chunks used to produce a response? Perhaps metadata or indexes can be added to the chunks which the LLM can use when generating a response.

[spheroid]

Chris, great video as always. I learn so much from your channel, thanks. One thing I at least didn't quite "get" from this - where you talk about vectorization and embeddings - what actually *is* that process? The general concept I understand - turn the chunk into a numerical vector and compare them for similarity - but the vectorization itself - what is JinaAI doing at that point and how does it overcome e.g. the challenges of mismatching vocab between the question and the knowledge chunk without being externally trained itself on a bunch of stuff? Or maybe the embeddings are based on some other training from elsewhere? Was just a bit hazy on that point... maybe a thought for a future video if you're inclined :)

[Kopp]

Thank you for a fantastic breakdown of RAG. I can now see why my Copilot trial at work is so bad at information retrieval. I'm guessing that as the queries get more complex and the spread of the data becomes wider, the less useful this method will become less effective. Does that push us toward a rolling fine-tune approach to a base model?

[juliussakalys]

Hi Chris, I appreciate the high quality content! Could you do a video or just give a simple reply on where are you taking your expertise from? Maybe some communities, projects or anything of the sort. Personally (and I am sure that other people as well), I would like to become proficient in basically the same things that you are an expert at when it comes to engineering solutions for AI related problems :)

[path]

If you want facts you need to pump the determinism by lowering the temperature.