## Vectara Recognized in two 2025 Gartner® Market Quadrants for Generative AI

Sean Anderson

Vectara recognized for model development and AI knowledge management

## Get Diverse Results and Comprehensive Summaries with Vectara’s MMR Reranker

Today, we’re incredibly excited to announce that we’re releasing a new “reranking” capability built into Vectara: one that’s focused on increasing the amount of diversity of the results near the top of the result list.

## Why is Metadata Important in GenAI?

Today, we’re happy to announce that Vectara’s hybrid search capabilities have just gotten even stronger with the ability to apply updates to which metadata fields can have filters and what their data types are. This blog will walk through some common use cases and how you can use this new feature.

## Customers Don’t Like Your Chatbot. Here’s How to Make Them Love It.

AI has raised the stakes for customer communications with your Brand. Are you hitting the mark? Or do you want to make sure your strategy isn’t a failure to launch. Learn what makes a smart chatbot

## Measuring Hallucinations in RAG Systems

Vectara launches open-source Hallucination Evaluation Model (HEM) that provides a FICO-like score for grading how often a generative LLM hallucinates in Retrieval Augmented Generation (RAG) systems.

## Cut the Bull... Detecting Hallucinations in Large Language Models

Vectara launches the open source Hughes Hallucination Evaluation Model (HHEM) and uses it to compare hallucination rates across top LLMs including OpenAI, Cohere, PaLM, Anthropic’s Claude 2 and more.

## Top Large Language Models (LLMs): GPT-4, LLaMA 2, Mistral 7B, ChatGPT, and More

The top large language models along with recommendations for when to use each based upon needs like API, tunable, or fully hosted.

## Retrieval Augmented Generation (RAG) Done Right: Retrieval

In RAG, retrieving the right facts from your data is crucial, and choosing the right embedding model to power your retrieval matters!

## Learning to Measure AI Search with Vectara

AI-powered search is everywhere – new projects, products and companies are appearing to solve age-old challenges. But how ‘good’ is an AI-powered search engine – and how can we measure this?

## Using Hybrid Search to Improve In-App Product Search

Hybrid search has the power to vastly improve in-app experience allowing users to find what they are looking for quickly, and allows tuning between semantic and lexical search.