The development of large language models (LLMs), like ChatGPT, Bard, Anthropic, is generating a lot of interest with implications at many business levels. One of the most promising advancements of this technology is providing the enterprise with human expertise at a machine scale. This is where also the challenge comes with Generative AI’s ability to hallucinate and provide inaccurate responses. To overcome these challenges and unlock the real value of generative AI, enterprises can use their business data to receive answers in context. Progress MarkLogic and Progress Semaphore can solve this problem by providing a semantic knowledge graph long-term memory solution in the form of a scalable and secure knowledge data platform. Semantically relevant use case specific data can be used by AIs for retrieval augmented generation (RAG).
Dive into the realm of AI data-driven enterprises. Join this presentation hosted by Imran Chaudhri, Chief Architect, AI, Healthcare & Life Sciences at Progress, who will provide an overview of the fundamental concepts of LLMs and demonstrate how companies can extend their capabilities, providing them an unbounded long-term memory. By augmenting LLMs with knowledge retrieved from long-term semantic memory, companies can ensure more trustworthy and accurate results.
Learn how merging generative AI with enterprise data can transform your business strategy, increase productivity, reduce costs, and deliver high-quality and trustworthy results. In this presentation, you will:
- Explore the benefits of Generative AI tools in your organization
- Understand how you can achieve trustworthy and accurate results by using LLM models
- Understand how you can add context and meaning to your enterprise data ahead of your LLMs to improve results
- Discover how you can gain LLM-independent associative long-term secure private data memory