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Blog - Retrieval Augmented Generation - Page 2
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End RAG Sprawl: The case for platform standardization
As enterprises rush to integrate Retrieval-Augmented Generation (RAG), many are encountering RAG Sprawl—a fragmented, resource-intensive approach that leads to security risks, inefficiencies, and mounting technical debt.
The Importance of Explainability in Enterprise RAG
In the era of generative AI, trust and transparency are essential—especially for enterprises in regulated industries where AI hallucinations can have serious consequences. Vectara’s Enterprise RAG platform prioritizes explainability with citation-backed responses, advanced observability tools, and chat history search, ensuring AI-driven insights are verifiable and reliable.
Top Enterprise RAG predictions
How Enterprise RAG has evolved and what we can expect to see next...
Why building your own RAG stack can be a costly mistake
Think building your own Retrieval-Augmented Generation (RAG) system will give you a competitive edge? Think again. Companies are sinking months and hundreds of thousands of dollars into in-house AI projects, only to end up with sluggish, unsecure, and overpriced systems that can't hold a candle to existing solutions.
You’re Not an Engineer. Why Should You Care About RAG?
Before we tackle the question at hand, let me provide some background on my journey to Vectara and how I reached this conclusion
Generative AI Adoption Roadblocks, Pit Stops, Crashes, and Destinations
When building something new and testing the art of the possible, crashes are bound to happen. Not just for Ferraris but for all new technology, and especially with Generative AI…
Vector Database: Do You Really Need One?
RAG Platforms’ Silent Takeover of Vector Databases
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