Evaluating RAG System Performance
Learn metrics and techniques to assess the accuracy, relevance, and coherence of your RAG application's responses.
Why Evaluate RAG Systems?
When you build a Retrieval Augmented Generation (RAG) application, it's crucial to know if it's working as intended. Evaluation helps us understand if our system is providing accurate, relevant, and helpful answers.
Without proper evaluation, it's hard to tell if changes to your RAG pipeline (like new embedding models or chunking strategies) are actually making it better or worse.
Key Aspects of RAG Evaluation
Evaluating a RAG system involves looking at different aspects. We generally focus on:
- Retrieval Quality: Are the right documents being found?
- Generation Quality: Is the LLM producing good answers based on those documents?
These two parts are often evaluated separately and then together to get a full picture.
All lessons in this course
- Integrating All RAG Components
- Querying and Generating Answers
- Evaluating RAG System Performance
- Building a Golden Test Set for RAG