What is Agentic RAG?

Discover the future of AI-driven conversations with Agentic RAG. This powerful pipeline enhances responses from large language models by incorporating relevant data retrieved from vector databases. Join David Levy as he discusses how Agentic RAG can create more responsive, accurate, and adaptable AI systems to better service fields like customer service, legal tech, and beyond.

[norbertschmidt]

You make the best LLM + RAG explainers in the world. Thanks so much.

[nbamastermind]

Excellent explanations! Simple without additional fluff. Thank you.

[lesmoe]

Fantastic description, it seems using agents to help you route to potentially different databases is a game changer, thank you.

[sqweepsrussell]

Thanks for this well explained video. This is the most simplified explanation of agentic rag that sank into my grey matter

[ErickM.Joseph]

Makes sense I’ll be implanting this into our agent workflow

[BizAutomationU]

I asked Perplexity how best to decide if seperate vector DBs is preferred over a large vector DB with metadata to contextualize the pool of vector data. I guess it's tantemount to asking if we should normalize vector tables similar to OLTP or use a data warhouse approach. It recommended the metadata approach to help the LLM decide which portion of the vector data to contextualize, which makes sense to me. Maybe it's the same thing described in this video in a different way.

[jaffarbh]

Thanks. This is pretty useful and much better than the naïve approach of overloading the model with lot's of irrelevant data from the vector DB.

[PriyeshYadav]

So the Agent is also a pretrained LLM with those 2 vector db ???

[Ilovepotatoes-tt]

thank u!! One question: Do you know how to evaluate an agentic rag? Do I have to take anything into account compared to a normal rag evaluation? (e.g. with RAGAs..) Best regards

[RohitGulati]

Im confused ... Andrew Ng says just RAG is Agentic AI. But RAG does not need LLM during the query phase. So what's correct here?

[sjmediaonline]

IBM’s main task is now to create the catchup teaching videos. No innovation or breakthroughs are coming out from them. I see so many IBM old timers are watching with amazement how smaller open source innovators are moving lightning fast in GenAI and then they start recording teaching videos… I do not see any new things / new demos coming out from IBM.

[DintlP]

We need to know how to implement agent and what is this? is this another llm to determine the context and route to right db

[MikewasG]

What is the difference between this and semantic routing?

[Ijmeisner]

In the reflection I think that’s the teleprompter… or maybe I am hallucinating 🤣

[hiwifi-s]

“More responsible, more accurate, more adaptable, “
plus more secure as well?

[AK-bejh]

So the agent will acts like a controller here .

[marcomaiocchi]

This pipeline doesnt make a lot of sense.

[amotorcyclerider]

Jesus. He did not even say how the agent uses the llm to select the right data source. He did not even say how agent is implemented. So incomplete.. this video is incomplete. Delete and present with a complete one.

[funkfreeze]

Overkill for most consumer facing applications and, as the answer to generalist queries, not specific enough a system for internal tooling. Lots of noise introduced here.