Elastic: Context engineering that works: Production-grade RAG with Contextual AI and Elastic

Retrieval augmented generation (RAG) has rapidly evolved from a novel concept to an essential enterprise capability. As data volumes grow, traditional RAG implementations have revealed limitations that are now being addressed through next-gen approaches.

Join RAG pioneers and vector database leaders to explore the concept of RAG 2.0. Learn how context-aware architectures deliver deeper semantic understanding, more accurate retrieval, and personalized responses that adapt to user intent and org-level frameworks.

This webinar will explore how Contextual AI, using Elastic's cutting-edge technology, is achieving remarkable accuracy and scalability with RAG. Elastic's vector database is pivotal for managing the vast and varied data volumes required by Contextual AI's enterprise clients.

We will walk through Contextual AI's journey from foundational concepts to a production-ready RAG architecture, touching upon the evolution toward RAG 2.0 and agentic workflows. The session will highlight key Elastic features that have been instrumental in Contextual AI's success, including ease of use, flexible deployment options, and powerful core capabilities.

Highlights

• High-performance RAG at scale: Contextual AI achieves 90%–95% accuracy with RAG 2.0, processing millions of documents and over 12 million chunks through custom pipelines and fine-tuning.
• Elasticsearch as production vector database: Elasticsearch provides hybrid search (BM25 + embeddings), multi-cloud deployment, comprehensive APIs, and proven scalability for complex enterprise applications.
• RAG 2.0 and agentic future: Contextual AI's advanced architecture includes Contextual Language Models and vision for agentic workflows with tool usage and enhanced decision-making capabilities.