In this article, you will learn how to evaluate LLM applications using the three dominant open-source frameworks — RAGAS, DeepEval, and Promptfoo — and why the LLM-as-a-judge mechanism they all rely on has measurable biases you need to actively design around.
Topics we will cover include:
How RAGAS, DeepEval, and Promptfoo differ in purpose and when to use each one, including which pairings experienced teams converge on.
How to implement a faithfulness check and a CI-gated quality evaluation with working code you can run immediately.
What position bias, self-preference bias, and verbosity bias are, how to detect them with an audit harness, and how to mitigate them in production.
