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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The new Vectara Admin Center provides a centralized, intuitive interface for on-premise and VPC customers to manage their Vectara deployments. It empowers DevOps and IT Admin teams with centralized visibility and control to prevent RAG Sprawl overhead.
How Enterprise Deep Research can shorten RFP processing time from weeks or days to hours or even minutes.
Deep Research for the Enterprise is poised to become the killer app of generative AI, unlocking a powerful way to explore nuanced, data-rich questions for decision making by tapping directly into their own document repositories.
Context Engineering is emerging as the evolution of prompt engineering. Yet larger context windows can backfire: too many tokens invite noise, contradictions, and diminishing returns.
In highly regulated enterprise environments, predictability is non-negotiable: the same question to your RAG application should return consistent answers every time. With Open-RAG-Eval’s new Consistency-Adjusted Index, you can now precisely quantify that level of reliability.
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.
AI agents have the power to transform enterprise workflows—but without proper safeguards, they can easily go off course. In this blog, we explore the growing need for Guardian Agents like Vectara’s Hallucination Correction Agent
HCMBench is Vectara’s open-source evaluation toolkit designed to rigorously test and compare hallucination correction models. With modular pipelines, diverse datasets, and multi-level evaluation metrics, it gives developers a powerful, standardized way to measure and improve the accuracy of RAG system outputs.
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