RAG Framework Explained: How Live Data Drives AI Builders, Designers, and Users in 2025

[@RagnarPitla]

Thank you for watching my video, if you have any suggestions please reach out! Join me her www.youtube.com/@RagnarPitla?sub_confirmation=1 If you enjoyed the content and want to stay updated with more insights on AI, innovation, and productivity, don't forget to subscribe to my channel using the link below. Your support means the world to me! Also, let's connect on LinkedIn for even more conversations and updates-I'd love to hear your thoughts and ideas. www.linkedin.com/in/ragnarpitla/

[@MohammedAnnus]

very informative sir thank you

[@RagnarPitla]

I am glad you found it helpful!- thanks for watching @MohaMmedAnnus

[@moore.mindful]

Another excellent video packed full of useful content!

[@RagnarPitla]

Thanks for the positivity!

[@ianfoster99]

Really helpful video. Thank you for making it. Already incorporated RAG into my platform. MemGPT is now on my list

[@RagnarPitla]

Thanks @ianfoster99 - its everywhere and knowing what type works is key going forward. Thanks for the comment here.

[@karaebelofficial]

Awesome video and production :)

[@RagnarPitla]

Thanks for the kind words, I appreciate it.

[@mohamedmaf]

Thanks a lot, very interesting video

[@RagnarPitla]

Thanks, glad you found it interesting!

[@AIWALABRO]

Simply Saying Thankyou for this awesome content! can you do provide the codes . e.g you talking about rag with agentic ai. it will much more helpful for us.

[@RagnarPitla]

Thanks a bunch for the idea! I’m currently working on some examples that we can build end-to-end for later videos. It takes me some time to research, build, and think about what works best, but I’m super motivated by the comments and positive feedback like yours! I really appreciate it, and I’ll make sure to include it in the next two or three videos. Have a fantastic day!

[@AIWALABRO]

@@RagnarPitla that also good one , ya pls do provide the coding till deployment. how exactly things are works in the llm (end-to-end)wants to see. pls take vertexai or aws or azure.

[@gjagadishbabu]

@RagnarPitla - Great explanation on RAG and types. For text to SQL on 100+ tables/1000 fields , which RAG type and LLM would you suggest especially where we are looking for more accurate results in a regulatory domain ( biotech Clinical safety, manufacturing process)? I was looking at Numberstatikn LLM and some vector DB combination.

[@RagnarPitla]

Kindly connect with me on LinkedIn, and I would be delighted to provide you with further insights and recommendations based on the additional information you provide.

[@Tamara-Jost]

Thank you for the introduction about RAG 🙏 It got me thinking: How does RAG manage data retrieval at scale? Does it rely on pre-configured integrations with specific data sources, or can it dynamically scale to query and adapt to new datasets via APIs or other mechanisms?

[@RagnarPitla]

Hey Tamara! That’s a great question! Yes, it can scale, but the key is to make sure the vector indexing is effective and efficient.

[@VaydaMaymeTheo]

Thank you Ragnar, I need to learn this for my job. Can you point me toward any training for it?

[@RagnarPitla]

Thanks! @vaydaMaymeTheo - if possible reach me on LinkedIn, I am not aware of any specific training on RAG but most AI courses will cover.

[@ianfoster99]

Search for Learn Microsoft AI videos on KZbin. Lots of great oversight stuff and coding examples stuff

[@RagnarPitla]

Sure, thanks

[@Karl6.5CM]

Interesting thoughts about MemGPT's future use cases

[@RagnarPitla]

MemGPT is one of the many fascinating areas of development in this space- I am still reaserching and learnign how we are using it with some good exampels. thakns for the comment, appreciate it.

[@RakeshKumar-if6oc]

@RagnarPitla In Education sector esp. for personalized learning which RAGs should be used? Memory RAG?

[@RagnarPitla]

@Rakeshkumar-if6oc In the education sector, Memory RAG stands out as a powerful tool for personalized learning. By incorporating a persistent memory component, it can store and retrieve past interactions or facts, delivering coherent and customized outputs. Have you looked at Khanmigo? This capability makes it particularly effective in adapting to each student’s unique progress and learning needs, creating a more engaging and tailored educational experience. Moreover, advanced RAG models such as EMG-RAG (Eigen Memory Graph RAG) and GEM-RAG (Graphical Eigen Memories RAG) take personalization to the next level. These models dynamically retrieve and organize educational content based on individual student performance, preferences, and learning paths, ensuring a highly adaptive and responsive learning environment