FeaturedRetrieval Augmented Generation
Think building your own Retrieval-Augmented Generation (RAG) system will give you a competitive edge? Think again. Companies are sinking months and hundreds of thousands of dollars into in-house AI projects, only to end up with sluggish, unsecure, and overpriced systems that can't hold a candle to existing solutions.
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How developers can build GenAI conversational search applications with Vectara’s Retrieval Augmented Generation
While generative AI is a powerful technique that promises to revolutionize how we interact with computers, it has serious drawbacks. Vectara’s Retrieval Augmented Generation approach addresses those drawbacks, making generative AI safer and more cost-effective to use.
Get accurate, dependable answers from your data with fewer hallucinations
Will code-generating LLMs replace developers? Probably not anytime soon
What are code-generating LLMs? Helping developers be more productive
A collection of crawlers for the Vectara community, making crawling and indexing documents quick and easy
LLM hallucinations and how to avoid them with Grounded Generation
A sample app demonstrating how to build a search experience powered by Vectara
Large Language Models and the applications they power, like ChatGPT, are all over the news and our social media discussions these days. This article cuts through the noise and summarizes the most common use cases to which these are successfully being applied.
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