OpenAI Cookbook: Evaluating RAG systems

We’re excited to unveil our OpenAI Cookbook, a guide to evaluating Retrieval-Augmented Generation (RAG) systems using LlamaIndex. We hope you’ll find it useful in enhancing the effectiveness of your RAG systems, and we’re thrilled to share it with you.

The OpenAI Cookbook has three sections:

1. Understanding Retrieval-Augmented Generation (RAG): provides a detailed overview of RAG systems, including the various stages involved in building the RAG system.
2. Building RAG with LlamaIndex: Here, we dive into the practical aspects, demonstrating how to construct a RAG system using LlamaIndex, specifically applied to Paul Graham’s essay, utilizing the VectorStoreIndex.
3. Evaluating RAG with LlamaIndex: The final section focuses on assessing the RAG system’s performance in two critical areas: the Retrieval System and Response Generation.

We use our unique synthetic dataset generation method, generate_question_context_pairs to conduct thorough evaluations in these areas.

Our goal with this cookbook is to provide the community with an essential resource for effectively evaluating and enhancing RAG systems developed using LlamaIndex.

Join us in exploring the depths of RAG system evaluation and discover how to leverage the full potential of your RAG implementations with LlamaIndex.

Keep building with LlamaIndex!🦙