Why RAG Evaluation Matters
RAG systems evolve quickly—indexes, prompts, rerankers, and model versions all change. Without rigorous evaluation you risk regressions and inconsistent answers. Our approach blends offline testing with online telemetry to maintain quality and confidence.
New to RAG? Start with the What is RAG? primer or see our RAG Development Services and RAG Tech Stack.
Core Metrics & Methods
- Retrieval Precision/Recall: Do top-k passages contain the needed facts?
- Groundedness: Are key claims supported by citations?
- Answer Quality: Accuracy, completeness, and clarity scoring.
- Latency & Cost: P95 response time and token/infra costs.
- Golden Sets: Curated query–citation–answer triplets from real users.
- Heuristics & LLM Judges: Automated checks plus human spot reviews.
- A/B Tests: Compare retrievers, prompts, and rerankers safely.
- Dashboards & Alerts: Continuous monitoring to catch regressions.
Evaluation Workflow
1) Define
Agree on KPIs and acceptance criteria by use case and stakeholder needs.
2) Build
Create golden sets, automated checks, and dashboards; integrate CI/CD gates.
3) Iterate
Run A/Bs, tune retrieval/prompts, and ship improvements confidently.
Frequently Asked Questions
Retrieval precision/recall, groundedness, answer quality, latency, and user satisfaction. Use offline tests for safety and online tests for business impact.
A curated set of representative queries with expected citations and answers. It anchors offline evaluation and prevents regressions during iteration.
Require citations for key claims and verify the cited text supports the answer. Score groundedness across the golden set with human review and heuristics.
Run automated offline tests on every change and monitor online metrics continuously. Schedule deeper human reviews weekly or each sprint.
Yes. We provide evaluation pipelines, dashboards, and annotation workflows tailored to your data and KPIs.