メインコンテンツにスキップ

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

無料アカウントを作成する

練習とトラックであなたの進捗を記録しましょう。

Googleで登録続行すると、次に同意したものとみなされます： 利用規約、 プライバシーポリシー 、およびデータが米国内に保管されることに同意したものとみなされます。

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

トピック

Artificial Intelligence

関連

ブログ

Advanced RAG Techniques

もっと見る →