Скачать или смотреть Testing Agentic RAG — Retrieval Accuracy, Source Grounded Answers, and Multi Step Workflow Assurance

“Agentic RAG” is where Retrieval-Augmented Generation (RAG) meets tool-using agents—and it demands new ways to test. In this practical, 5-minute episode of the “Model to Agent” series, we show the essentials of how to evaluate and harden RAG systems with IR-grade retrieval metrics (Precision@K, Recall@K, MRR, nDCG), LLM-as-a-Judge scoring for faithfulness and answer relevancy, and multi-step agent assurance across Plan→Retrieve→Reason→Act→Observe loops (e.g., ReAct). You’ll see how to:
• Build evaluation datasets from real user queries and slices;
• Add RAGAS / DeepEval checks to CI/CD;
• Set pre-deploy gates (e.g., nDCG@10 and Faithfulness thresholds);
Future episode will continue into:
• Instrument observability for retrieval, prompts, tool I/O, citations, latency, and cost;
• Roll out with canaries, shadow runs, and human-in-the-loop checkpoints.

If your RAG only “feels” good, it won’t scale. Measure it, ship it, and keep it grounded in sources.
👉 Learn more at Omniit.ai about AI-drive testing, AI/Agent evaluation, and beyond in the modern QE world.

References & further reading:
• Lewis et al., Retrieval-Augmented Generation (RAG) (arXiv) — foundations of RAG.
• NVIDIA explainer on RAG (2025).
NVIDIA Blog
• IR metrics (Precision/Recall, MRR, nDCG) overviews.
Weaviate
• RAGAS metrics (faithfulness, answer relevancy).
Ragas
• LLM-as-a-Judge best practices & survey.
arXiv
• ReAct agent paradigm (reason + act).
arXiv