As organizations are moving generative AI projects from incubation to production, they are focusing on three fundamental aspects of LLMs: improving the quality of the GenAI models’ output, the security and protection of enterprise data and IP investments, as well as balancing rampant implementation costs.
To improve the accuracy, explainability and efficiency of generative AI models and their ability to surface relevant information, businesses need to leverage best-of-breed data technologies based on knowledge graph and hybrid search through retrieval augmented generation (RAG).
Join the Progress MarkLogic product team for an in-depth exploration of the next-generation search capabilities coming to MarkLogic Server 12 and learn about:
- Building a flexible RAG architecture that matches your data requirements
- Using the latest features, including storing and searching vector embeddings, to enable hybrid search and re-ranking of retrieval results
- What’s coming next in MarkLogic Server 12
Learn firsthand how the MarkLogic comprehensive search suite allows you to perform a composable, hybrid search in a single query to retrieve and re-rank results with maximum relevance and improve the accuracy of GenAI responses.
Watch now!