In this episode of Memriq Inference Digest — Engineering Edition, we dive deep into rigorous evaluation strategies for Retrieval-Augmented Generation (RAG) systems. Drawing from Chapter 9 of Keith Bourne’s book, we explore how quantitative metrics and visualizations help AI engineers optimize retrieval and generation performance while managing cost and complexity.

In this episode:

- Why continuous, multi-metric evaluation is critical for RAG pipelines post-deployment

- Comparing dense vector similarity search versus hybrid search with real metric trade-offs

- Automating synthetic ground truth generation using LLMs wrapped in LangChain

- Building modular, scalable evaluation pipelines with ragas and visualization tools

- Practical challenges like cost management, dataset size limitations, and the role of human evaluation

- Real-world use cases in finance, research, and customer support that benefit from rigorous evaluation

Key tools & technologies mentioned:

- ragas (open-source RAG evaluation framework)

- LangChain (model and embedding wrappers)

- matplotlib and pandas (data visualization and manipulation)

- ChatOpenAI (LLM for generation and evaluation)

Timestamps:

0:00 – Introduction and episode overview

2:30 – The importance of continuous RAG evaluation

5:15 – Hybrid vs dense similarity search: metric comparisons

9:00 – Under the hood: ragas evaluation pipeline and LangChain wrappers

13:00 – Visualizing RAG metrics for actionable insights

16:00 – Practical limitations and balancing cost with thoroughness

18:30 – Real-world RAG evaluation examples

21:00 – Open challenges and future directions

23:30 – Final thoughts and book spotlight

Resources:

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

- Visit Memriq.ai for more AI engineering deep-dives, tools, and resources