Evaluating RAG System Performance
Learn metrics and methodologies to assess the quality and effectiveness of your RAG applications.
Why Evaluate RAG Performance?
You've built a Retrieval Augmented Generation (RAG) system. But how do you know if it's actually good? This lesson teaches you how to measure its effectiveness.
- RAG systems combine information retrieval with large language models (LLMs).
- Evaluation helps you understand strengths, weaknesses, and areas for improvement.
- It's crucial for building reliable and accurate AI applications.
Key Evaluation Goals
Evaluating a RAG system means looking at two main components: the retrieval part and the generation part.
- Retrieval Quality: Is the system finding the most relevant information (context) for the user's query?
- Generation Quality: Is the LLM producing accurate, relevant, and coherent answers based on the retrieved context?
- Ultimately, we want to measure the overall user experience and answer quality.
All lessons in this course
- Query Transformation Techniques
- Multi-Stage RAG Pipelines
- Evaluating RAG System Performance
- Reranking Retrieved Results