Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) are two distinct yet complementary AI technologies. Understanding the differences between them is crucial for leveraging their ...

As developers look to harness the power of AI in their applications, one of the most exciting advancements is the ability to enrich existing databases with semantic understanding through vector search ...

AI is undoubtedly a formidable capability that poses to bring any enterprise application to the next level. Offering significant benefits for both the consumer and the developer alike, technologies ...

BERLIN & NEW YORK--(BUSINESS WIRE)--Qdrant, the leading high-performance open-source vector database, today announced the launch of BM42, a pure vector-based hybrid search approach that delivers more ...

Open-source vector database provider Qdrant has launched BM42, a vector-based hybrid search algorithm intended to provide more accurate and efficient retrieval for retrieval-augmented generation (RAG) ...

Have you ever found yourself frustrated with AI systems that confidently provide answers, only to realize they’re riddled with inaccuracies? It’s a common pain point for anyone working with generative ...

Even though traditional databases now support vector types, vector-native databases have the edge for AI development. Here’s how to choose. AI is turning the idea of a database on its head.

Retrieval-augmented generation (RAG) has become a go-to architecture for companies using generative AI (GenAI). Enterprises adopt RAG to enrich large language models (LLMs) with proprietary corporate ...

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Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More More companies are looking to include retrieval augmented generation (RAG ...