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Ed Enuff, Chief Product Officer at DataStax, discusses the integration of AI in vector databases like Cassandra and the challenges and opportunities it presents. They explore the role of data management in relation to AI, including labeling, storage, and retrieval mediums. The concept of retrieval augmented generation (RAG) is explored to eliminate hallucinations in language models. The chapter also highlights AstraDB, a cloud product built on Cassandra, and the importance of benchmarks in evaluating database relevancy.
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Quick takeaways
- Vector databases like DataStacks are instrumental in minimizing the hallucination problem in AI applications by retrieving trusted data into large language models (LLMs).
- Combining the scalability of Cassandra with the retrieval capabilities of vector databases like DataStacks enables businesses to achieve faster, more accurate insights and improved customer experiences in AI applications.
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The Role of Vector Databases in AI Applications
Vector databases, such as DataStacks, are being increasingly used in AI applications to address the hallucination problem with large language models (LLMs). By retrieving trusted data into LLMs through vector databases, the problem of hallucinations can be minimized. Vector databases store and query vectors, which are numerical representations of concepts and ideas. These databases work well with LLMs as they enable the retrieval of relevant content, reducing hallucinations. DataStacks provides vector database capabilities, and their cloud product, AstraDB, is experiencing significant growth, with a large portion of new signups using it for vector usage.
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