Unlock the power of continuous evaluation for Retrieval-Augmented Generation (RAG) systems in this episode of Memriq Inference Digest - Leadership Edition. We explore how quantitative metrics and intuitive visualizations help leaders ensure their AI delivers real business value and stays relevant post-deployment.

In this episode:

- Why RAG evaluation is a continuous, critical process—not a one-time task

- Key metrics for measuring retrieval and generation quality, including precision, recall, and faithfulness

- Comparing similarity search vs. hybrid search retrieval approaches for different business needs

- How synthetic ground-truth data and AI-driven evaluation frameworks overcome real-data scarcity

- Visualization tools like matplotlib transforming complex metrics into actionable leadership dashboards

- Real-world use cases and a debate on retrieval methods for customer support AI

Key tools & technologies mentioned: ragas, LangChain, OpenAI Embeddings, matplotlib

Timestamps:

0:00 - Introduction & episode overview

2:30 - The importance of continuous RAG evaluation

5:15 - Understanding retrieval and generation metrics

8:45 - Similarity vs. hybrid search: business trade-offs

12:00 - Synthetic ground truth and automated evaluation pipelines

15:30 - Visualizing performance for leadership insight

17:45 - Real-world impacts and tech showdown

19:30 - Closing thoughts and next steps

Resources:

- "Unlocking Data with Generative AI and RAG" by Keith Bourne - Search for 'Keith Bourne' on Amazon and grab the 2nd edition

- Explore more at https://memriq.ai