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Building and Evaluating RAG Pipelines

Learn how to construct the RAG pipeline, evaluate response quality, and apply it to real-world use cases.
Abi Aryan
Abi Aryan

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Retrieval-Augmented Generation (RAG) pipelines are essential for building intelligent chatbots that can provide accurate, context-rich responses. Turning this concept into a working application requires hands-on experience with tools, techniques, and evaluation methods. In this session, you’ll bring it all together by building a real-world AI chatbot designed to answer questions about the New York Stock Exchange.

In this session, Abi Aryan, Founder at Abide AI, will walk you through how to build a RAG-powered chatbot using Python. You’ll learn how to construct the RAG pipeline, evaluate response quality, and apply it to real-world use cases. This session is ideal for AI engineers who want to deepen their RAG skills with a practical, high-value application.

Key Takeaways:

  • Learn how to build a chatbot and RAG pipeline in Python.
  • Discover techniques for evaluating the quality of chatbot responses.
  • Explore a real-world use case of a New York Stock Exchange AI chatbot.

Session Resources

Темы

Artificial Intelligence

Связанное

блог

Advanced RAG Techniques

Учебное руководство

Agentic RAG: Step-by-Step Tutorial With Demo Project

Учебное руководство

Llama 4 With RAG: A Guide With Demo Project

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