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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Today, we’re proud to announce 2 significant enhancements to our generative response capabilities. These new features are aimed at significantly improving the developer experience as well as giving Vectara administrators better ability to review and analyze conversations their users have had with the system.
GPT4o and Gemini-1.5-Flash are fast and cheap, but hallucinate more
Legal minds are being empowered with Generative AI models to revolutionize their legal practices. From AI Assistants and Conversational AI, to getting answers from legal content: I urge managing partners and senior partners to embrace this innovation as the present and future of law is being defined by Generative AI and legal minds.
A recap of this years Data Council conference and the Lightning Track that Vectara hosted
When running Gen AI applications, it is important to decide whether to build or buy the RAG infrastructure on which to run your application. This article helps you to make that decision.
Featuring multilinguality, unlimited context window, and calibration – The Hughes Hallucination Evaluation Model (HHEM) v2 is a major upgrade from v1
Why the size of the model does not necessarily determine its likelihood to hallucinate
Leveraging Vectara’s API to build Sankofa, a browser extension that allows anyone to chat with their web content history
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