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

Nina Lopatina

Lead Developer Advocate, Contextual AI

Elastic

Jordi Mon Companys

Principal Product Marketing Manager, Platform

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 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 context engineering best practices and how to build reliable 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: Teams using Contextual AI achieves 90%–95% accuracy with RAG 2.0, processing millions of documents and over 27 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.
• Context engineering and AI’s agentic future: Learn how high fidelity context engineering can enable advanced agentic workflows with enhanced document understanding and query capabilities.
• Demos: See Contextual AI’s RAG platform in action on complex unstructured data, all built on Elasticsearch vector database.

Additional resources

• Learn about RAG
• Advanced RAG techniques
• The most widely deployed, open source vector database
• Intro to Contextual AI for specialized RAG agents