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

"If businesses are going to depend on this thing, somebody needs to solve the problem of hallucination." – Amr Awadallah, CEO & Co-Founder at Vectara 🔗 https://lnkd.in/gV37qeY3 This show was created in collaboration with HumanX. #ai #tech #SoTPodcasts

DeepSeek-R1 and OpenAI’s Deep Research just changed the game—but in different ways. DeepSeek’s 30x cheaper reasoning model is forcing enterprises to rethink AI deployment. The result? A massive shift toward RAG, distillation, and custom models. Instead of relying on monolithic AI systems, companies are realizing that armies of smaller, task-optimized models will dominate the future. 🔹 Distillation & Smaller Models → The days of running massive, inefficient LLMs for everything are over. DeepSeek AI is proving that distilled, domain-specific models are the way forward. As Sam Witteveen puts it: The reasoning models are thinking too much. Companies need models that take decisive action, especially when it comes to agentic applications. 🔹 Supervised Fine-Tuning (SFT) → If your domain knowledge isn’t publicly available, SFT is your move. Just like IBM engineer Chris Hay fine-tuned a math model that could out-think OpenAI’s o1, companies in niche industries (think shipbuilding, proprietary finance companies, etc) need to train their own models on their own data. 🔹 Reinforcement Learning (RL) for Personality → Ethan Mollick calls it: Every model is getting good at everything. But what makes them different? RL is how companies train AI to match their tone, personality, and brand voice. 🔹 RAG: The Pragmatic Choice for Most Enterprises → Ground your models in real, proprietary data instead of trying to fine-tune everything. Vector databases + AI = precise, controlled responses. As Amr Awadallah from Vectara highlights, DeepSeek still hallucinates 14% of the time vs. 8% for OpenAI’s o3. RAG is the guardrail. 🔹 The Cost of AI is Crashing—Fast → We all expected costs to drop, but not this fast. Dario Amodei at Anthropic estimates a 4x annual decline in model costs. And as Ashok Srivastava at Intuit said: “I fully expect the cost to go to zero... and the latency to go to zero.” The AI economy is shifting from scarcity to abundance—and that changes everything. So what does this mean? ✅ Expect waves of specialized, task-optimized models ✅ Open-source is gaining momentum in enterprise AI ✅ Data quality > model choice (just ask Hilary Packer, CTO at AmEx) 💡 Big takeaway: The AI winners won’t just use LLMs—they’ll orchestrate them. 📺 Watch my YouTube video on all this, where I also invite Sam Witteveen to give a brief rundown of how RAG works: 👉 https://lnkd.in/gjUk4UTw 📖 Read the full VentureBeat article break-down here: 👉 https://lnkd.in/g_XwenPR Would love to hear your thoughts—what’s your AI strategy in this new era? 👇 #AI #GenerativeAI #DeepSeek #OpenAI #RAG #LLMs OpenAI

📢  Starting Soon! 🚀 Join 1,000+ global AI developers at the first-ever industry event: DeepSeek+ Forum, where we dive into DeepSeek AI: Unleashed & Integrated! The outstanding lineup of speakers: Ivan 🥁 Nardini and Kris (Google) | Suman Debnath (AWS) | Laurie Voss (LlamaIndex) | Jon Peck (GitHub) | Ofer Mendelevitch (Vectara) 📅 Date: Feb 6th, 9AM PST | Virtual 📍 RSVP: https://lnkd.in/gHQECgU3

⚙️ AI Agents: Your new productivity powerhouse! AI Agents take AI Assistants to the next level by acting on behalf of users—executing tasks like scheduling meetings or automating routine actions. Learn how to build safe and effective AI Agents with the Executive RAG Cookbook. 📖 What you’ll discover: ✅ How AI Agents identify, prioritize, and execute actions ✅ Ways to safely deploy action-taking tools ✅ Tips for integrating Agents into your workflow 👉 Get your free copy today! https://bit.ly/4fKOe7n #GenerativeAI #RAGaaS #EnterpriseAI #Vectara #Developers #AgenticRAG

We just added the Nova models from Amazon Web Services (AWS) to Vectara's Hallucination Leaderboard. They are all pretty decent and below 2%. HHEM: https://lnkd.in/gkYJ-wNA Leaderboard: https://lnkd.in/g9Wi_8Sh

DeepSeek-R1’s Hallucination Problem—Can It Be Fixed? The AI community has been buzzing about DeepSeek-R1 and its advanced reasoning capabilities. But our research at Vectara uncovered a critical concern: a 4x increase in hallucination rate compared to DeepSeek-V3. Using Vectara’s HHEM model and Google’s FACTS evaluation, we found that while R1 is more capable, it also generates more false information. But is this tradeoff unavoidable? Or can better training techniques reduce hallucinations while preserving strong reasoning? Our latest analysis explores this tension and what it means for the future of AI development. 📖 Read the full breakdown by Forrest Sheng Bao, Ofer Mendelevitch, and Chenyu Xu: https://bit.ly/3PYeX5i DeepSeek AI

Vectara’s Head of Developer Relations, Ofer Mendelevitch, is taking the stage at AI Dev World 2025! 🎤 Session: Building Enterprise-Ready RAG 📍 In-Person: February 11-13, 2025 | Santa Clara Convention Center, CA 💻 Virtual: February 18-20, 2025 Discover how Retrieval-Augmented Generation (#RAG) can power scalable, high-accuracy, and secure AI solutions for enterprises. If you're a developer or data scientist, this is your chance to stay ahead of the game in Generative AI! 🔗 Secure your spot now: https://lnkd.in/ej8_wpaG