Key Metrics for RAG Performance
Understand and apply relevant metrics like precision, recall, context relevance, and faithfulness to evaluate RAG outputs.
Why Evaluate RAG Performance?
When building Retrieval Augmented Generation (RAG) systems, it's not enough to just deploy them. We need to know if they're actually working well!
Evaluating RAG is more complex than evaluating a standalone Large Language Model (LLM) because it involves two main stages: retrieval and generation.
RAG's Unique Evaluation Needs
Traditional LLM evaluation metrics often focus on the quality of generated text, like fluency or coherence. But RAG systems have specific goals:
- To provide answers grounded in facts.
- To avoid 'hallucinations' (making up information).
- To use only relevant information from your data.
This requires a special set of metrics.
All lessons in this course
- Key Metrics for RAG Performance
- Developing Evaluation Benchmarks
- A/B Testing and User Feedback Loops
- Detecting and Measuring Hallucinations