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/Evaluating AI Applications Using LlamaIndex
Evaluating AI Applications Using LlamaIndex
Learn how to evaluate different components of an LLM application and iteratively improve system performance using LlamaIndex’s built-in evaluation tools.
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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 retrieved results.
If the correct document is ranked 1st, the reciprocal rank is 1.0.
If it’s ranked 2nd, the score is 0.5, and so on.
MRR averages this score over all test queries.
LLM-based scoring (for subjective quality of responses) ...