Although retrieval augmented generation is a very popular architectural pattern for delivering Q&A over complex internal information, it is also extremely hard to build a high quality, high accuracy RAG system. Come to this session to learn the 7 gotchas of RAG systems and best practices to improve their performance.

As organizations scale GenAI from concept to production, they face challenges like ensuring accuracy, explaining responses, and connecting GenAI to unique knowledge. This session shows how GraphRAG combines knowledge graphs with retrieval-augmented generation to build GenAI apps grounded in enterprise data. Learn how companies like Klarna have deployed GenAI to build chatbots grounded in knowledge graphs, improving productivity and trust, while a major gaming company achieved 10x faster insights. We'll share real examples and practical steps for successful GenAI deployment.

AstraZeneca has implemented a "platform" approach, which serves as a centralized repository of standardized, enterprise grade, reusable services and capabilities that are accessible to AI factories. This platform includes user interfaces, APIs that integrate AI services with enterprise systems, supporting resources like data import tools and agent orchestration services. AstraZeneca will share how, starting with a few generative AI use cases, they have successfully identified common services and capabilities, subsequently standardizing these elements to maximize their applicability through the platform. These solutions leverage technologies like GPT models, Natural Language Processing and Retrieval Augmented Generation (RAG) architecture.

Getting AI/ML innovation out of the lab and into production means operationalising and scaling complex pipelines, and ensuring that the resulting signals are pushed into operational workflows where they can be actioned. We will briefly introduce Teradata’s extensive AI/ML capabilities and provide examples of how customers leverage them. We will also discuss Agentic AI, what it means for the data platform - and how Teradata’s industry-leading vector store is enabling customers to deploy scalable RAG applications.

Despite the popularity of retrieval-augmented generation, organizations are struggling to optimize large language model applications based on RAG. Attend this session to get suggestions and recommendations on improving your RAG solution, LLMOps for RAG systems, and scaling considerations.