The Developer’s Guide to RAG with Data Streaming

Retrieval-augmented generation (RAG) helps combat hallucinations and other inaccuracies that affect LLM content generation. However, RAG must be implemented in a way that provides accurate and up-to-date information and in a governed manner that can be scaled across applications and teams. The Developer’s Guide to RAG shows why an event-driven architecture is the best fit for this.

Read this ebook to learn how to use a data streaming platform across four patterns of RAG application development.

Highlights:

• Discover the benefits of building a streaming RAG architecture
• Learn how to implement data augmentation, inference, workflows, and post-processing
• Understand how to use connectors, Flink (incl. stream processing, AI Model Inference, Table API, UDFs, Actions) and stream governance for RAG
• See RAG use cases and reference architectures