FeaturedHallucination
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.
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Is Semantic Chunking worth the computational cost?
Discover why fixed-size chunking often outperforms semantic chunking in real-world RAG systems. This study explores the trade-offs between simplicity, computational efficiency, and retrieval accuracy, challenging the assumption that semantic chunking is always the superior choice.
Vectara at GITEX Dubai: Powering the Future of Generative AI and RAG
Vectara takes center stage at GITEX Dubai, showcasing the future of Generative AI and RAG technology in one of the world's premier innovation hubs.
RAG with User-Defined Functions Based Reranking
Using UDF-based reranking for fine-grained control over your search results with Vectara
Building a RAG Pipeline is Difficult
Overview The best RAG systems utilize many different types of models (embedding model, generative LLM) to achieve the best, and highest quality results. When you build a small RAG POC,…
Celebrating 2 Million Downloads of HHEM
Vectara’s Hallucination Evaluation Model surpasses 2 million downloads as the fight against LLM hallucinations continues
Automating the Back Office: Enhancing Efficiency and Experience at Startups
In the fast-paced world of startups, back-office tasks like HR, finance, and IT can become overwhelming. That’s where automation steps in, not just as a tool for efficiency but as a way to free up time for innovation and creative work
Why I Joined Vectara: Catching the Generative AI Rocket Ship
Rather than continuing my career solely in the banking sector, Vectara offers me the chance to participate directly in this revolution wherever it creates the most value
Introducing Vectara’s Chain Rerankers
Vectara adds new powerful capabilities to allow rerankers to be “chained” together to give you a balance of business rules and neural reranking
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