A Comprehensive Guide
Best Practices in RAG Evaluation
Learn how to assess, calibrate, and optimize your RAG applications for long-term success.
What you will learn
The guide covers:
- Recommended frameworks for comprehensive RAG assessment
- How to identify and solve common RAG performance issues
- Techniques for working with custom datasets
- Essential metrics to monitor during testing, and more.
Download the guide
How to evaluate a RAG system
This guide will teach you how to evaluate a RAG system for both accuracy and quality.
Stages prone to errors
You will learn to maintain RAG performance by testing for:
- Search precision
- Recall
- Contextual relevance
- Response accuracy.
Information retrieval
This stage involves searching and fetching relevant information from a knowledge base or external sources.
Information augmentation
In this stage, the retrieved information is processed and combined with the original query
Generating responses
Using the augmented information, the language model generates a response to the original query.
Why evaluate your RAG application?
The guide will outline both common issues, as well as recommendations to avoid these pitfalls.
Lack of Precision
Poor recall
“Lost in the middle”
Recommended evaluation frameworks
In the guide, we explore three popular frameworks that can help simplify your evaluation process.
Ragas is an open-source framework for evaluating retrieval augmented generation systems.
Quotient AI is a platform that focuses on building and deploying RAG systems.
Arize Phoenix is a tool designed for monitoring and observability in AI systems, including RAG pipelines.
Learn More
Learn how to test RAG with questions and answers, evaluate RAG pipelines with custom datasets, and visually deconstruct response generation by reading the guide.
Download the Guide
Read Qdrant’s Best Practices in RAG Evaluation guide for a deep dive into:
Why RAG evaluation is crucial for your AI's success
Recommended frameworks for comprehensive assessment
How to identify and solve common RAG performance issues
Techniques for working with custom datasets
Essential metrics to monitor during testing