Framework Feature Matrix
Promptfoo, DeepEval, RAGAS, LangSmith and Phoenix compared by what each is good at, with working examples and a way to choose.
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.
The pipeline
Each stage in order — click any step to read what it does.
Interactive Pipeline Flow
Architecture diagrams
RAGAS evaluation metrics for RAG pipelines.
Key design decisions
The trade-offs worth knowing before you build this.
RAGAS uses an LLM internally to decompose answers and evaluate statements. GPT-6.1 Sol produces more accurate evaluations than weaker models. Using a small, cheap model 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.
Continue reading
All AI Observability articles →Offline Evals: The Regression Suite for LLM Features
Build an evaluation dataset from real use, pick the cheapest grader that works, measure the noise floor, and turn the suite into a release gate.
Token Tracking and Cost Management
Monitor token usage and costs — per-request tracking, budgets, cost allocation across teams, and the optimization strategies that shrink the bill.
Phoenix: Open-Source LLM Observability
Arize's open-source, OTel-native LLM observability stack — traces, eval workflows, and prompt experiments, self-hosted with no data leaving your VPC.
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