Building and Evaluating RAG Pipelines: Part 1, Concepts
Key Takeaways:- Understand the core concepts of retrieval-augmented generation and how RAG pipelines work.
- Learn about architectural patterns used in real-world AI applications.
- Discover methods for evaluating and monitoring RAG systems.
Description
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.
Presenter Bio
Abi founded and runs the AI-assisted content creation platform Abide. She has a decade of experience as a machine learning scientist and engineer across e-commerce, insurance, and media. Abi is the author of the forthcoming book "LLMOps: Managing Large Language Models in Production".