FeaturedHallucinationNOV 6, 2023

Cut the Bull... Detecting Hallucinations in Large Language Models

Simon Hughes

Cut the Bull... Detecting Hallucinations in Large Language Models

Vectara launches the open source Hughes Hallucination Evaluation Model (HHEM) and uses it to compare hallucination rates across top LLMs including OpenAI, Cohere, PaLM, Anthropic’s Claude 2 and more.

Simon Hughes

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Application DevelopmentOCT 3, 2024

Building AI Assistants with Vectara-agentic and Arize

How to add Observability to your Vectara AI Assistants and Agents with Arize Phoenix

VectaraOCT 1, 2024

Vectara vs. The Rest (A Go-to-Market Perspective)

Vectara enables developers and businesses to easily embed generative AI into applications without needing data science expertise. As a serverless, end-to-end Retrieval-Augmented Generation (RAG) platform, it cuts deployment time from years to weeks. With proprietary LLMs, scalability, and cost efficiency, Vectara offers flexible deployment options, ensuring trust, control, and adaptability for businesses driving AI innovation.

VectaraSEP 17, 2024

Highlights from The AI Conference 2024: Vectara’s Impact and Industry Trends

Vectara sponsors The AI Conference in San Francisco where they presented on an Advanced RAG Panel and hosted a meetup on Build vs. Buy (RAG solution)

ResearchSEP 3, 2024

Correcting Hallucinations in Large Language Models

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).