Evaluating RAG Pipelines with MLflow: A Practical Framework

Building a RAG pipeline is the easy part. Knowing whether it actually works well — that is the hard part. Most teams deploy RAG systems with vibes-based evaluation: "the answers look good to me." That does not scale.

The Evaluation Framework

A proper RAG evaluation measures three things:

• Retrieval quality — Are you finding the right documents? Measure precision, recall, and MRR (Mean Reciprocal Rank).
• Generation quality — Given the right documents, is the answer accurate? Measure faithfulness (does the answer match the sources) and relevance (does it answer the question).
• End-to-end quality — Does the full pipeline produce answers that users trust? Measure completeness and correctness against a ground-truth test set.

MLflow Evaluate in Practice

Create a test set of 50-100 representative questions with expected answers. Run them through your RAG pipeline with MLflow tracking enabled. Use MLflow Evaluate to score each response. Track scores over time as you change chunking strategies, embedding models, or retrieval parameters.

The Key Insight

Evaluation is not a one-time activity. It is a continuous process. Every time you add new documents, change a model, or modify your prompts, re-run your evaluation suite. MLflow makes this repeatable and comparable.