RAG Benchmarkingv1.0.0
Plug in any RAG system — LangChain, LlamaIndex, or custom — and benchmark it against classic and agentic-era metrics. Faithfulness, answer relevancy, retrieval precision, and four agentic metrics for multi-step agents. Measured faithfulness of 0.958 on the 50-sample golden dataset.
Apache 2.0 · zero telemetry · source Regulation (EU) 2024/1689 (Article 15).
Why RAG Benchmarking exists
Accuracy, robustness and cybersecurity
Article 15 becomes enforceable on 2 August 2026 for high-risk AI systems under Annex III. Providers must declare accuracy metrics in the instructions for use and demonstrate consistent performance across the lifecycle; non-compliance via the Article 16 provider-obligation chain is sanctionable up to €15M or 3% of global annual turnover under Article 99(4). For RAG-based high-risk systems, "appropriate accuracy" is not a self-asserted figure — it is a metric declared on the label and defensible against post-market evidence.
When the findings land on a governance desk
Tools surface problems. Programmes solve them.
RAG Benchmarking hands you the file. The work that follows — programme design, board narrative, regulator engagement — is what AskAjay.ai (the advisory arm of AI Exponent LLC) does.
Explore advisory at AskAjay.ai →Other AI Exponent flagship tools