Evaluating AI Applications Using LlamaIndex
Explore how to evaluate the retrieval accuracy of AI systems built with LlamaIndex. Understand key performance metrics like hit rate and mean reciprocal rank, and learn to use RetrieverEvaluator tools to measure and improve retrieval quality for robust AI applications.
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When building LLM applications—whether it’s a RAG pipeline, a chatbot, or a multi-step agent—generating output is only part of the job. What really matters is: is the output useful?
To answer that, we need evaluation.
Evaluation lets us test system components like retrieval, response generation, or prompting against real expectations. It helps us track what’s working, catch silent failures, and compare different design choices.
LlamaIndex provides tools to evaluate individual parts of the pipeline using:
Metrics like hit rate and MRR (for retrieval)
Hit rate: It measures whether at least one of the expected (ground truth) documents was retrieved for a query.
Hit Rate = 1.0 means success (a relevant result was found).
Hit Rate = 0.0 means failure (no relevant results returned).
MRR (Mean Reciprocal Rank): It evaluates how early the correct document appears in the list of ...