Building and Evaluating RAG Pipelines: Part 1, Concepts

Retrieval-Augmented Generation (RAG) is a foundational technique for building more accurate, efficient, and context-aware AI applications. But to effectively use RAG in production, engineers need a clear understanding of its architecture, best practices, and evaluation methods. Without the right foundations, RAG pipelines can become fragile, unscalable, or hard to trust.

In this first session of a two-part series, Abi Aryan, Founder at Abide AI, will introduce the core concepts behind RAG and its role in modern AI systems. You’ll learn about key RAG pipeline components, common architecture patterns, and the principles of evaluating and monitoring RAG performance. This session is ideal for AI engineers looking to build robust, real-world AI applications powered by retrieval-enhanced generation.