The Complete RAG Playbook (Part 4): Evaluation & Choosing What Works

Author(s): Ravi Kumar Verma Originally published on Towards AI. The Complete RAG Playbook (Part 4): Evaluation & Choosing What Works We’ve covered 19 RAG techniques across three parts. You’ve seen chunking strategies, context enrichment, query transforms, rerankers, and advanced architectures. But there’s one question nobody likes to answer: Image by author using GeminiThis article focuses on the evaluation of RAG techniques, outlining the importance of measurement to determine which implementation works best for specific use cases. The author discusses building proper evaluation datasets, implementing relevant metrics, and benchmarking existing methods while analyzing their performance. Through honest evaluation, readers are equipped with the knowledge to make informed decisions regarding which techniques to deploy in diverse situations, ultimately encouraging a practical approach to algorithm selection. Read the full blog for free on Medium. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI