FeaturedRetrieval Augmented Generation
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.
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Introducing the Open RAG Benchmark: A revolutionary, multimodal dataset built from arXiv PDFs to elevate RAG system evaluation with real-world text, table, and image understanding.
The Open Evaluation website makes it easier for you to spot the patterns within an Open RAG Eval evaluation report and take effective steps to improve your RAG system.
Every year, governments and agencies roll out thousands of pages of new rules, policies, and updates. For businesses and public institutions trying to keep up, it’s become a massive, expensive problem.
AI Assistants and Agents are revolutionizing financial services, offering hyper-personalized customer experiences, operational efficiencies, and enhanced risk management. Explore how Vectara’s GenAI and RAG-powered platform enables financial institutions to embrace this new wave of intelligent, autonomous technology securely and effectively.
Open RAG Eval is an open-source framework that lets teams evaluate RAG systems without needing predefined answers, making it faster and easier to compare solutions or configurations. With automated, research-backed metrics like UMBRELA and Hallucination, it brings transparency and rigor to RAG performance testing at any scale.
Vectara's new open-source framework for comprehensive RAG evaluation, open-rag-eval, represents a significant leap forward in ensuring that AI systems deliver accurate, relevant responses.
History is repeating itself in AI. Just as enterprises once built siloed databases before embracing centralized data platforms, today’s rapid adoption of Retrieval-Augmented Generation (RAG) is leading to a fragmented mess of custom-built AI systems—what we call RAG Sprawl.
RAG Sprawl—the unchecked proliferation of Retrieval-Augmented Generation implementations across your organization—is silently eroding your AI investments. This growing problem is causing headaches for CIOs and IT departments while draining resources, compromising security, and creating inconsistent user experiences.
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.
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