Rag Evaluation Frameworks
Learn the industry-standard metrics and frameworks for evaluating the accuracy of your RAG system.
How do you know if your RAG system is actually accurate? You can’t just rely on intuition. You need a rigorous evaluation framework that quantifies performance.
The "RAG Triad" Metrics
Measure the triad of RAG success: Context Relevance (did the database retrieve the right info?), Groundedness (did the model rely *only* on the retrieved context?), and Answer Relevance (did the model actually answer the user’s question?).
Using "LLM-as-a-Judge"
Modern evaluation uses high-performance models (like Claude 3.5 or GPT-4o) as a "judge" to score your RAG outputs. By providing the judge with the query, context, and output, it can provide an objective, score-based assessment of how accurate your system really is, enabling you to track improvements over time.