RAG Architecture | Scalable Architecture for LLMs

[@ravideviceregunta5794]

The Best RAG architecture video ever seen…. Lot of people are complicate the concept a lot…
Could you do another video with the how prompt is compared with embeddings and measured accurate results…

[@TechPrimers]

Sure ravi. Will do

[@abhijitprusty]

What is required to be a prompt engineer , it would be really nice if you could share some info. Once again, great to see your vides again

[@TechPrimers]

Sure Abhijit. It needs a mix of datascience and AI knowledge. Take a look at this datasciencedojo.com/blog/prompt-engineer/#:~:text=5%20essential%20skills%20for%20becoming%20a%20prompt%20engineer,knowledge%20...%205%205.%20Data%20analysis%20experience%20. Glad to be back😀

[@ravi1341975]

Option 4: Enhancing search results with relevant context

[@TechPrimers]

Spot on! Ravi!

[@java-aws-techie]

Option 3 - Translating text between languages

[@TechPrimers]

Unfortunately no. Read through the options again. Clue lies in the texts itself 😁

[@java-aws-techie]

Got it - option 4: Enhancing search results with relevant context

[@blasttrash]

I think Option - 2 coz he used that in example-2 earlier.

[@TechPrimers]

Actually its option 4, since RAG helps in both enhancing search results (by the LLM) and with relevant Context (by the Retrieval mechanism).

[@ramkarlapudi662]

Option 4

[@amithraju830]

Very difficult to understand need more simplification

[@TechPrimers]

Sure Amith. When we get to handson part this will get better

[@sayanstrsc.7272]

🙏 Please remove that "thang thang" bell sound on subscribe request! Please remove that 🙏 Not useful, rather annoying, doesn't suit this channel or the kind of viewers of this channel!

[@TechPrimers]

done buddy. changed in all the new videos :)