Developing Evaluation Benchmarks
Create custom datasets and benchmarks to systematically test and compare different RAG configurations and improvements.
Why RAG Benchmarks Matter
Welcome! In this lesson, we'll learn how to create custom evaluation benchmarks for your RAG systems. Benchmarks are like custom test sets that help you measure how well your RAG application performs.
They are crucial for understanding improvements, regressions, and ensuring your RAG system delivers accurate and relevant information to your users.
Custom Benchmarks: The Why
While public datasets like SQuAD or HotpotQA are great for general LLM evaluation, they often don't reflect your specific use case or domain.
- Domain Specificity: Your RAG needs to answer questions about your data.
- Nuance & Complexity: Public datasets might not capture the unique challenges your users face.
- Continuous Improvement: Custom benchmarks allow you to track performance against your evolving needs.
All lessons in this course
- Key Metrics for RAG Performance
- Developing Evaluation Benchmarks
- A/B Testing and User Feedback Loops
- Detecting and Measuring Hallucinations