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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A deeper dive on why DeepSeek R1 hallucinates more than DeepSeek V3
Vectara's Intelligent Query Rewriting (Tech Preview) improves RAG search accuracy by separating queries into their natural language and filter expression components.
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.
DeepSeek has shaken the AI industry with its release of Deepseek-R1, but it turns out that Deepseek-R1 has a hallucination problem.
How Enterprise RAG has evolved and what we can expect to see next...
A Typescript-native interface for Vectara, simplifying Integration for Developers
With Vectara's new Knee Reranking, you can automatically improve result quality and reduce GenAI latency
Vectara’s Hughes Hallucination Evaluation Model (HHEM) now supports 8 languages, expanding from English, German, and French to include Portuguese, Spanish, Arabic, Chinese - Simplified, and Korean.
A Python-native interface for Vectara, built for developers.
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