Author(s): Florian June Originally published on Towards AI. Typical RAG Process, Best Practices for Each Module, and Comprehensive EvaluationThe process of  ...

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Towards AI

Explores the process of retrieval-augmented generation (RAG) and outlines best practices for its various components. Discusses query classification, efficient document retrieval, re-ranking for relevance, re-packing into structured formats, and summarization to extract key information. The post also provides a comprehensive evaluation of these practices and concludes with insights and recommendations.

The Best Practices of RAG

How is there a best practice ? This is a fastly improving field. Every choices have their pros and cons (latency, cost, accuracy).
And why is it limited to vector db ? In reality R in RAG can be in any form (SQL, web search, cypher search, and so on)

Little irritating that this link points to a site with a summary and a link to medium. But I do like the content. I also think the title is misleading, that being said, it was informative. Definitely read some things that I would like to test in my own use cases.