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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In this blog post, we share the results of our initial experiments aimed at correcting hallucinations generated by Large Language Models (LLMs). Our focus is on the open-book setting, which encompasses tasks such as summarization and Retrieval-Augmented Generation (RAG).
Before we tackle the question at hand, let me provide some background on my journey to Vectara and how I reached this conclusion
A no-code environment for chat with your documents, powered by Vectara
Today, we’re incredibly excited to announce user-defined sorting functions for Vectara!
Enabling advanced RAG applications with Vectara Agentic
The upgraded HHEM-2.1 outperforms both GPT-3.5-Turbo and GPT-4 for hallucination detection and is powering our updated HHEM leaderboard.
Discover how Vectara’s Retrieval-Augmented Generation (RAG) capabilities elevate Incorta’s Nexus platform, enabling organizations to effectively harness the power of GenAI with precise and contextually relevant data.
Thank you to some of our local employees and investors for attending our July team building lunch.
In response to growing enterprise concerns over data security and the quality of retrieval-augmented generation (RAG), Vectara is proud to introduce Mockingbird, an LLM fine-tuned specifically for RAG. Mockingbird achieves the world’s leading RAG output quality and hallucination mitigation, making it perfect for enterprise RAG and autonomous agent use cases.
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