FeaturedResearch
Vectara recognized for model development and AI knowledge management
Read moreFeaturedResearchCategories
Blog - page 3
All posts
Introducing Enterprise Deep Research
Deep Research for the Enterprise is poised to become the killer app of generative AI, unlocking a powerful way to explore nuanced, data-rich questions for decision making by tapping directly into their own document repositories.
Context Engineering: Can you trust long context?
Context Engineering is emerging as the evolution of prompt engineering. Yet larger context windows can backfire: too many tokens invite noise, contradictions, and diminishing returns.
Is Your RAG Consistent?
In highly regulated enterprise environments, predictability is non-negotiable: the same question to your RAG application should return consistent answers every time. With Open-RAG-Eval’s new Consistency-Adjusted Index, you can now precisely quantify that level of reliability.
Open RAG Benchmark: A New Frontier for Multimodal PDF Understanding in RAG
Introducing the Open RAG Benchmark: A revolutionary, multimodal dataset built from arXiv PDFs to elevate RAG system evaluation with real-world text, table, and image understanding.
Open Evaluation
The Open Evaluation website makes it easier for you to spot the patterns within an Open RAG Eval evaluation report and take effective steps to improve your RAG system.
Trusted AI: the important role of guardian agents
AI agents have the power to transform enterprise workflows—but without proper safeguards, they can easily go off course. In this blog, we explore the growing need for Guardian Agents like Vectara’s Hallucination Correction Agent
HCMBench: an evaluation toolkit for hallucination correction models
HCMBench is Vectara’s open-source evaluation toolkit designed to rigorously test and compare hallucination correction models. With modular pipelines, diverse datasets, and multi-level evaluation metrics, it gives developers a powerful, standardized way to measure and improve the accuracy of RAG system outputs.
Vectara's Hallucination Corrector
AI hallucinations create significant business risks and erode user trust. Vectara's Hallucination Corrector (VHC) identifies inaccuracies, suggests fixes, and provides essential guardrails for your AI applications.
New HHEM and OpenAI chat completions endpoints
We’ve created a trusted platform for building safe and reliable AI applications and continue to invest in features that improve reliability, accuracy, and flexibility. Today, we're taking another step forward by introducing two powerful new capabilities: the Hallucination Evaluation Model (HHEM) and OpenAI Chat Completions endpoints.
Before you go...Connect with
Connect with
our Community!
- Join us on Discord
Discord.
Connect our community channel.
- Join us on Github
Github.
Join our discussion channel.
- Follow us on X / Twitter
X / Twitter.
Get news, company information.
- Follow us on LinkedIn
LinkedIn.
Adopt best practices in projects.
- Join us on Discuss
Discuss.
Suggest your own ideas.
- Write us on E-mail
E-mail.
Ask your follow-up questions.
