RAG Explained
HTML-код
- Опубликовано: 6 май 2024
- Get the interactive demo → ibm.biz/BdmPEb
Learn about the technology → ibm.biz/BdmPEp
Oftentimes, GAI and RAG discussions are interconnected. Learn more about about RAG is and how it works alongside your databases, LLMs and vector databases for better results with Luv Aggarwal and Shawn Brennan.
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Very simple and clear explanation.. cheers to IBM
Shawn & Luv!!!! Awesome job!!!!
Hey! If I ask a RAG-based language model, "Tell me the features of the iPhone 17," what will it tell me? Will it say it doesn't know or will it hallucinate? I understand that once the iPhone 17 is released, the database will be updated to provide the correct information. But what happens if I ask about it before its release?
Thanks for the straight forward description of RAG.
Excellent video, love it
Thanks guys, very clear!
Neat and detail explanation.
Interesting , thanks both
Thank for sharing !!
Does not address how do you validate the Q1 results returned are accurate. You should have built in a process parallel to querying the LLM of actually querying the results and training the LLM to address any discrepancies, if that is possible or correct them.
nice work.
Awesome video
Cool explanation
Good job. I still need to learn more about data accuracy in a LLM.
Informative
There is good point of halucinations of AI and the video unfortunately does not address it. The data governance is not addressing this issue we still can have a scenario where input is valid but output generated by AI is a garbage.
What's the solution?
@@vintastic_you have to make sure the relevant data is going to the model. Good info into the data base is only half the battle. Semantic chunking. Size of chunks..types of search. Type of vector database used. For example PG Vector is a Postgres plugin and is not near as good at retrieval (usually) as something like pinecone
Then the prompt used can also tremendously affect the model. You have to put it in the right context and use industry specific terms when prompting. Even a genius needs context or a bit of time to think. No matter who good the model, you have to know a bit about the specific industry to obtain great results. It’s like explains a noise to you mechanic or telling them you have a miss-fire on cylinder 1.
@@vintastic_ human expert review for every response
ok, gotcha 👌
Gotcha.
👏👏
🤔I think Luv saw the connection the entire time
Nobody's ever been fired for buying RAGs from IBM.
... yet
kabhi haans bhi liya karo..
(Smile a bit bros)
How in the world is this dude writing inverted for us too read straight lol
they mirror the video) you can notice that most of the people writing on a glass board are left handed(in reality 90% of planet’s population is right handed), that’s also because they mirrored the video
It's a skill only left-handed people have
And then a wide spread global epidemic crisis is brought to light wherein our gold standard "books" (peer reviewed journals) are rife with bad and corrupt data due to mismatched incentivization and misalignment of directives; and we then realize...how much good data through science do we really have? Shame we polluted the books we are supposed to be able to trust now that we have this magnificent technology here. 😭
Dude, keep on topic. This isn’t the place for your grievances
Boring