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Gen AI Platform Build vs Buy – Part I: Options and Tradeoffs
When running Gen AI applications, it is important to decide whether to build or buy the RAG infrastructure on which to run your application. This article helps you to make that decision.
HHEM v2: A New and Improved Factual Consistency Scoring Model
Featuring multilinguality, unlimited context window, and calibration – The Hughes Hallucination Evaluation Model (HHEM) v2 is a major upgrade from v1
Do Smaller Models Hallucinate More?
Why the size of the model does not necessarily determine its likelihood to hallucinate
How Sankofa was Built with Vectara
Leveraging Vectara’s API to build Sankofa, a browser extension that allows anyone to chat with their web content history
Building GenAI Enterprise Applications with Vectara and Datavolo
Building a Datavolo data flow to process and index documents and messages into Vectara’s GenAI Platform
Say Goodbye to Delays: Expedite Your Experience with Stream Query
Discover how Stream Query reduces latency by delivering Gen AI responses in real-time chunks, eliminating the frustration of waiting for LLM’s response
Evaluating RAG with RAGAs
How to measure the quality of your Vectara RAG pipeline with RAGAs
April Fools Prank: New No-hallucinations LLM
A new LLM achieves 0% hallucinations and is set to revolutionize RAG
Automating Hallucination Detection: Introducing the Vectara Factual Consistency Score
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
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