Vectara

?? 3 Million Downloads and Counting! The momentum is unstoppable! Vectara’s Hughes Hallucination Evaluation Model (HHEM), the engine behind Vectara’s Factual Consistency Score, has officially hit 3 MILLION downloads on Hugging Face! This milestone is more than just a number—it’s a powerful signal that enterprises and developers are prioritizing accuracy, trust, and scalability in AI. As GenAI continues to evolve, reducing hallucinations isn’t optional—it’s essential. Thank you to the growing global community pushing the boundaries of what's possible with RAG AI Assistants and advancing safer, more reliable AI systems. We’re proud to stand alongside you in this mission. Check out the leaderboard today! https://bit.ly/3vejcTw #GenAI #ReliableAI #HallucinationDetection #Developer #RAGaaS #AIForEnterprise

Only 1 Week Left! Atlanta, This is Your AI Moment Time is running out to join RAG on the Road: Atlanta on March 18. If you're serious about AI, this is where you need to be. Connect with top minds in the industry, explore the latest in retrieval-augmented generation (#RAG) and AI-powered assistants, and gain insights that will shape the future of AI. Opportunities like this don’t come often—secure your spot before it’s too late. ?? March 18 | 3:00 PM EDT | Atlanta ?? Register now: https://bit.ly/3EtBW5s #GenAI #RAGaaS #LLMs #AIassistant #AIagent #TechCommunity #AIInnovation #Networking #Vectara #RAGontheRoad

?? The Challenge of AI Hallucinations: Ensuring Trustworthy AI ?? AI has transformed natural language processing, allowing machines to generate human-like text, summarize information, and engage in sophisticated conversations. Yet, even the most advanced AI models face a persistent challenge—hallucinations. These occur when AI generates responses that seem plausible but are factually incorrect or entirely fabricated. The root cause? AI models predict text based on patterns, not verified facts. Hallucinations arise due to biases in training data, probabilistic inference, and model overconfidence. Studies show that hallucination rates in LLMs vary widely—from 0.7% to 29.9%—highlighting the complexity of the issue. Addressing AI hallucinations requires a multi-pronged approach: ? Improving training data by reducing biases and enhancing accuracy ? Refining model architectures to enhance contextual understanding ? Leveraging external verification via real-time fact-checking and retrieval-based generation Companies like Mayo Clinic are pioneering solutions, integrating Reverse RAG (Retrieval-Augmented Generation) to ensure AI outputs are traced to authoritative sources. As AI continues to shape industries like healthcare, law, and finance, ensuring reliability isn’t optional—it’s essential. How do you see AI hallucinations impacting trust in AI-driven decisions? Let’s discuss and share your thoughts on the 103rd edition of my newsletter #TechTalk. ?? #AI #ArtificialIntelligence #MachineLearning #ML #NLP #TechEthics #AIEthics #EthicalAI #AIHallucinations #RAG #LLM #AIModels #MachineHallucinations #AITrust Vectara Amazon Web Services (AWS) Mayo Clinic Patronus AI OpenAI

If you are here, in Vegas for #HumanX2025, then come see my talk today (Wed) at 3:30pm pacific on the Enterprise Stage. I am also doing a roundtable on the headaches of "RAG sprawl" at 2pm in Azure 8 on Level 4. HumanX

Last Call! Dr. Amr Awadallah, CEO and Founder of Vectara will be speaking at HumanX Las Vegas today! Plus, he’s leading a roundtable at 2:00 PM PT in Azure 8 (Level 4) to discuss the challenges of “RAG sprawl”—a must-attend for AI innovators! Want to connect 1:1 with Amr and explore how Vectara’s AI solutions can elevate your business? Book a meeting now before spots fill up! ?? ?? Schedule here: https://bit.ly/3DkZUj7

?? AI Agents: The Future of Work is Here AI isn’t just a trend—it’s transforming the way businesses operate. From streamlining workflows to enhancing decision-making, AI agents are unlocking new levels of productivity and efficiency. Join us for an exclusive session where we will explore how your company can hire AI agents to drive success. ?? Date: Thursday, April 3 ?? Live on Zoom ?? Register now: https://bit.ly/3DvIdxk ?? Event details: https://bit.ly/41IQOpH

Vectara’s Postman Collection is Here! Building GenAI applications just got easier. With Vectara’s Postman collection, developers can now seamlessly integrate with our Enterprise #RAG API—no complex setup, no boilerplate code, just powerful AI at your fingertips. ?? Quickly test API calls for document indexing, querying, and chat ?? Streamline workflows with re-rankers, contextual configurations, and factual consistency scoring ?? Experiment with real-time AI directly in Postman’s user-friendly interface ?? Read the blog: https://bit.ly/4iIAyuA #Postman #Vectara #Partner #GenAI #RAGaaS #AIIntegration #APIs #DeveloperTools

I'm excited to share that Vectara's API is now available on Postman's network, making it even easier to use for developers. check it out here: https://lnkd.in/gfphuT5r Blog: https://lnkd.in/gJFrQTty Felipe Martinez Molina Patricia Dugan

Is Your Enterprise Ready for Agentic RAG? Join us at Data Council 2025, where Ofer Mendelevitch, our Head of Developer Relations, will break down the Build vs. Buy decision for Enterprise #RAG and show how to extend AI systems beyond basic retrieval into powerful, real-time problem-solvers. ?? Talk: Enterprise RAG: Navigating the Build vs. Buy Decision ?? Wednesday, April 23 Join us for a deep dive into Agentic RAG, where AI assistants go beyond simple query engines to leverage real-time data, APIs, and external tools for smarter, more adaptive AI solutions. Get your ticket here: https://bit.ly/4iAmzqB

Retrieval-augmented generation, like other workflows, requires careful design, development and operation. Does a commercial RAG product help? I enjoyed examining this topic in our new BARC report, "Build or buy RAG? Evaluating Your Options for GenAI with Retrieval-Augmented Generation." Thank you to our sponsor Vectara! Excerpt below. How does this fit your generative AI initiatives? Retrieval-augmented generation (RAG) has emerged as an efficient way to apply GenAI to proprietary enterprise data. RAG is a workflow that retrieves domain-specific content that relates to a user prompt, then uses it to augment the prompt and thereby help language models generate relevant and trustworthy outputs. New commercial software products can simplify RAG implementations by integrating and automating these workflows. To assess the value of such products, let's consider the team effort required to design, develop and operate a RAG workflow on your own. We examine the homegrown approach across the RAG lifecycle: design, development and operation. We define the people involved, the elements they manage and the processes they support. > Design Data and ML engineers gather RAG requirements based on their company’s business objectives and use cases for GenAI. They prioritize a wide range of requirements, including source datasets, preferred formats, AI/ML model techniques, performance SLAs, AI/ML model techniques and performance SLAs. Next they design the necessary software elements, including data pipelines and application tasks, and bring it all together in a comprehensive blueprint. > Develop Data engineers and ML engineers configure pipelines, perhaps using point tools, that ingest and trans-form the various data objects into AI-ready inputs. Developers build the application code that integrates pipelines, models and user interface. They test the resulting workflow, identify issues, then iterate. Ideally they treat each element as a microservice: modular, reusable and independent. > Operate Once in production, the real fun begins. Data engineers, ML engineers and developers operationalize functions such as data ingestion, data transformation, model input retrieval, prompt injection and model inference. They also roll into production with functions that support governance, observability and DevOps programs. They monitor how well all these functions perform in terms of latency, throughput and compliance with governance policies. And they optimize the workflow by updating code, fixing issues and adjusting hardware resources. Sometimes they return to the design phase and make structural changes. -------------------------- The decision to build or buy a RAG workflow hinges on an honest evaluation of your organization’s in-house expertise, infrastructure readiness, resource availability, and governance maturity. By addressing these factors, you can set the right path forward. #data #ai #genai #retrievalaugmentedgeneration