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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
Building AI Assistants with Vectara-agentic and Arize
How to add Observability to your Vectara AI Assistants and Agents with Arize Phoenix
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.
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)
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).
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
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