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Blog - page 11
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You’re Not an Engineer. Why Should You Care About RAG?
Before we tackle the question at hand, let me provide some background on my journey to Vectara and how I reached this conclusion
Introducing Vectara Portal
A no-code environment for chat with your documents, powered by Vectara
Introducing User-Defined Functions for Vectara
Today, we’re incredibly excited to announce user-defined sorting functions for Vectara!
Introducing Vectara-Agentic
Enabling advanced RAG applications with Vectara Agentic
HHEM 2.1: A Better Hallucination Detection Model and a New Leaderboard
The upgraded HHEM-2.1 outperforms both GPT-3.5-Turbo and GPT-4 for hallucination detection and is powering our updated HHEM leaderboard.
Power & Precision: Enhancing Operational GenAI with Retrieval-Augmented Generation from Vectara
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.
The Natural Diversity of Vectara: A Reflection of Our Culture
Thank you to some of our local employees and investors for attending our July team building lunch.
Mockingbird is a RAG-Specific LLM that Beats GPT 4, Gemini 1.5 Pro in RAG Output Quality
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.
Deep Dive Into Mockingbird: A RAG and Structured Output Focused LLM
In this blog post, we introduce Mockingbird, Vectara’s Retrieval Augmented Generation (RAG) and structured output focused LLM, and do a technical deep dive into its performance and discuss its technical capabilities.
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