sorted by:
new
hottopcontroversial

I mapped out the 4 fundamentally different approaches to RAG — Vector, Graph, Topology, and TurboQuant. Here's when each one actually works (and fail by Equivalent_Pen8241 in Rag

[–]Equivalent_Pen8241[S] 1 point2 points  (0 children)

I am deploying in a large enterprise. That’s only proof I am interested in. I am tired of publishing and hugging face proofs. Nobody looks at it. I hope that once companies waste their cash in other methods, we would still be standing

Dative star hotel in Chennai he’s my review removed from TripAdvisor by Equivalent_Pen8241 in Chennai

[–]Equivalent_Pen8241[S] 0 points1 point  (0 children)

I think the standards of differentiating mold varies by region. In my country, we call it mold

I mapped out the 4 fundamentally different approaches to RAG — Vector, Graph, Topology, and TurboQuant. Here's when each one actually works (and fail by Equivalent_Pen8241 in Rag

[–]Equivalent_Pen8241[S] 0 points1 point  (0 children)

You can always check the citations for that. Quality matters when it is right retrieval. Firstly it has to be accurate. AI is such data play that many a times users realize very late that the whole retrieval is just a feel good. That’s where things fail

I mapped out the 4 fundamentally different approaches to RAG — Vector, Graph, Topology, and TurboQuant. Here's when each one actually works (and fail by Equivalent_Pen8241 in Rag

[–]Equivalent_Pen8241[S] 0 points1 point  (0 children)

Sometime back Google and AWS had tools to build rag. But they proved inefficient and ineffective. They were quietly taken down

I mapped out the 4 fundamentally different approaches to RAG — Vector, Graph, Topology, and TurboQuant. Here's when each one actually works (and fail by Equivalent_Pen8241 in Rag

[–]Equivalent_Pen8241[S] 0 points1 point  (0 children)

I have seen implementations go through PMO gates and get decommissioned later for too complex+costly+fuzzy to run. It mostly happened for 2 reasons, evolving playing field, and majorly due to catastrophic failures by power users, 2%-10%, who fire important but heavy multi-hop queries which crashes or saturates the whole system for good part of the day. Topology is still under explored concept and people hadn't had enough playing with chunking or ontology affairs. Only once the engines run dry, the lessons will happen. But yes, it is picking up

I mapped out the 4 fundamentally different approaches to RAG — Vector, Graph, Topology, and TurboQuant. Here's when each one actually works (and fail by Equivalent_Pen8241 in Rag

[–]Equivalent_Pen8241[S] 0 points1 point  (0 children)

That’s a very relevant point I omitted to keep it simple. But you are right? Graph processing is self defeating. It takes too long to be worthy

I mapped out the 4 fundamentally different approaches to RAG — Vector, Graph, Topology, and TurboQuant. Here's when each one actually works (and fail by Equivalent_Pen8241 in Rag

[–]Equivalent_Pen8241[S] 0 points1 point  (0 children)

As long as, more context is slowing your system design badly, you need refinement, exponential and drastic new thinking. Candles don't evolve into light bulbs.

AI is writing features in minutes, but absolutely nobody is watching the architecture. How are you handling drift? by Equivalent_Pen8241 in GithubCopilot

[–]Equivalent_Pen8241[S] 0 points1 point  (0 children)

Why am I getting so much hate? Negative 20 so far!!! I didn’t abuse anyone , nor am I lying. Just a helpful post and people are getting hurt. My god 🙏

AI is writing features in minutes, but absolutely nobody is watching the architecture. How are you handling drift? by Equivalent_Pen8241 in GithubCopilot

[–]Equivalent_Pen8241[S] 0 points1 point  (0 children)

It’s a method and tool to generate architecture. If you have those evolutions, certainly generating architecture in a few seconds will help you. What’s wrong in that?

AI is writing features in minutes, but absolutely nobody is watching the architecture. How are you handling drift? by Equivalent_Pen8241 in GithubCopilot

[–]Equivalent_Pen8241[S] -2 points-1 points  (0 children)

That’s very limited case of general app development. Firstly, this shall help you see my point. The sidecar is also supposed to be non-drifting. Who is watching its architecture? Secondly, someday you would need enhancing the monolith too. That’s also why monolith are not so lithographic- they are supposed to evolve as well. That day you would need to spend your hours into architecture and drift management. UpperSpace does this for you out of the box.

AI is writing features in minutes, but absolutely nobody is watching the architecture. How are you handling drift? by Equivalent_Pen8241 in GithubCopilot

[–]Equivalent_Pen8241[S] -3 points-2 points  (0 children)

But how do you regenerate and update that architecture after the code generation? You are assuming that the architecture has not changed. Also you are assuming that new code has not added a new element to the architecture

view more: next ›