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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Building a Datavolo data flow to process and index documents and messages into Vectara’s GenAI Platform
Discover how Stream Query reduces latency by delivering Gen AI responses in real-time chunks, eliminating the frustration of waiting for LLM’s response
How to measure the quality of your Vectara RAG pipeline with RAGAs
A new LLM achieves 0% hallucinations and is set to revolutionize RAG
Vectara’s Factual Consistency Score (FCS) offers an innovative and reliable solution for detecting hallucinations in RAG. It’s a calibrated score that helps developers evaluate hallucinations automatically. This tool can be used by our customers to measure and improve response quality. The FCS can also be used as a visual cue of response quality shown to the end-users of RAG applications
How to run custom transformations on data from any Airbyte data source ingested into Vectara
Discover Vectara’s Four Principles for UI Development in the Age of AI.
See how Anthropic’s new Claude 3 LLM hallucinates compared to other foundation models in the Hughes Hallucination Evaluation Model (HHEM)
Vectara, the Trusted GenAI Platform for All Builders, recently completed its annual Penetration Testing as a part of its ongoing security commitment to its customers
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