Evaluation frameworks provide structured metrics and tooling for measuring LLM pipeline quality. RAGAS specialises in RAG evaluation: faithfulness (are claims grounded in context?), answer relevance (does the answer address the question?), context recall (did retrieval find relevant docs?). DeepEval provides a pytest-like interface for unit testing LLM outputs. Azure AI Evaluation adds Microsoft-specific safety metrics. Choose based on your pipeline type and hosting environment.
Each stage in order — click any step to read what it does.
RAGAS evaluation metrics for RAG pipelines.
The trade-offs worth knowing before you build this.
RAGAS uses an LLM internally to decompose answers and evaluate statements. GPT-4o produces more accurate evaluations than weaker models. Using GPT-3.5 as a judge can give misleading scores — the evaluator itself needs to be strong.
DeepEval imports into pytest. Each test case is a parametrized test over your dataset. Familiar CI integration — tests appear in pytest reports, CI passes/fails on threshold violations.
Generic metrics (faithfulness, relevance) miss domain-specific quality signals. Medical chatbot: is the response safe and risk-appropriate? Code assistant: does the code run? Financial: are all numbers accurate? Define custom judge prompts for your domain.
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