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In depth explanation + examples of why sometimes you don't need rag (especially with gemini 2.0)

34,228 次观看 • 1 年前 •via X (Twitter)

11 条评论

Sully 的头像
Sully1 年前

video up on youtube too

Aly Khairy 的头像
Aly Khairy1 年前

It's simpler, but still costs a lot more. Maybe break down 1 query into several prompts like "is management optimistic about iphone sales next Q" and feed that to a "risks"prmpr and an "mda" prmpt, retrieve more chunks then feed those chunks to Gemini to get a more rounded answer

Sully 的头像
Sully1 年前

Would you rather pay more and have a accurate answer ? I think the cost equation starts to matter less and less

Sohail Hosseini 的头像
Sohail Hosseini1 年前

Thanks for sharing!

Raduan Al-Shedivat 的头像
Raduan Al-Shedivat1 年前

amazing explainer video, Sully. one thing that you've missed, which is HUGE: **context caching**[1]. basically, now you can send this whole 50k transcript, cache it for subsequent calls, and never ever bother breaking this down for RAG, even for long conversations, because this whole thing will get cached. I am switching most of my LLM use to Gemini after this. [1]:

Sully 的头像
Sully1 年前

Agreed I didn’t even mentioned which makes it way cheaper I will say googles caching needs work

D33PS33K 的头像
D33PS33K1 年前

KAG vs RAG

David 的头像
David1 年前

great video, i just don't understand how google was able to achieve this without crazy hallucinations

Derek Cheung 的头像
Derek Cheung1 年前

Thanks Sully! Great work

Nate Pratt 的头像
Nate Pratt1 年前

im extracting information from bank statments and need it REALLY accurate what would you recommend?

Sully 的头像
Sully1 年前

Gemini

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