Ragas
Automated RAG evaluation framework with faithfulness, relevancy, and precision metrics for financial pipeline quality.
Ragas provides automated RAG evaluation metrics including faithfulness, answer relevancy, context precision, and context recall — all critical for validating financial RAG pipeline quality. The faithfulness metric specifically measures whether RAG answers are grounded in retrieved financial documents, preventing hallucinations about financial data. No ground truth requirement for several metrics makes Ragas the fastest way for finance teams to start evaluating RAG quality without building test datasets. For finance teams, Ragas enables quick, automated quality checks on financial RAG pipelines during development, helping catch issues before they reach production.
Key Features
- ✓Automated RAG evaluation metrics
- ✓Faithfulness scoring for financial RAG accuracy
- ✓Answer relevancy measurement for financial Q&A
- ✓Context precision and recall for financial retrieval
- ✓No ground truth needed for some evaluation metrics
Finance Use Cases
- Evaluate financial RAG pipeline before production
- Measure faithfulness of earnings call RAG answers
- Test context precision for regulatory document retrieval
- Compare RAG pipeline versions on financial benchmarks
- Quick financial RAG quality check during development
Pros
- ✓Simplest RAG evaluation tool to get started with
- ✓Faithfulness metric catches financial hallucinations
- ✓No ground truth dataset needed for initial evaluation
Cons
- ✗LLM-based evaluation can be expensive at scale
- ✗Less observability depth than LangSmith or Phoenix
- ✗Metrics can be noisy on short financial documents
Compatible Frameworks
Technical Details
Pricing
Prices are indicative and may vary.