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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Discover why fixed-size chunking often outperforms semantic chunking in real-world RAG systems. This study explores the trade-offs between simplicity, computational efficiency, and retrieval accuracy, challenging the assumption that semantic chunking is always the superior choice.
In this blog post, we share the results of our initial experiments aimed at correcting hallucinations generated by Large Language Models (LLMs). Our focus is on the open-book setting, which encompasses tasks such as summarization and Retrieval-Augmented Generation (RAG).
In this blog post, we introduce Mockingbird, Vectara’s Retrieval Augmented Generation (RAG) and structured output focused LLM, and do a technical deep dive into its performance and discuss its technical capabilities.
The top large language models in the Summer of 2024, along with recommendations for when to use each based upon needs like API, tunable, or fully hosted.
Learn how Legal teams can use Generative AI to safely increase efficiency, decrease costs, and improve the effectiveness of paralegal research
The top large language models along with recommendations for when to use each based upon needs like API, tunable, or fully hosted.
Today, researchers and analysts can be the beneficiaries of new ideas from massive research archives that may have never been possible in the past due to GenAI-powered hybrid search which can return pinpoint accurate results from 1,000’s of documents and sources, in any language and in an instant.
What the current GenAI stack looks like, and the role of emerging GenAI platforms like Vectara…
Which option is better for GenAI applications with your data
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