The Best Practices of RAG

Author(s): Florian June

Originally published on Towards AI.

Typical RAG Process, Best Practices for Each Module, and Comprehensive Evaluation

The process of RAG is complex, with numerous components. How can we determine the existing RAG methods and their optimal combinations to identify the best RAG practices?

This article introduces a new study titled “Searching for Best Practices in Retrieval-Augmented Generation”. This study aimes to address this problem.

This article is divided into four main parts. First, it introduces the typical RAG process. Next, it presents best practices for each RAG module. Then, it provides a comprehensive evaluation. Finally, it shares my thoughts and insights, and concludes with a summary.

Figure 1: Retrieval-augmented generation workflow. The optional methods considered for each component are indicated in bold fonts, while the methods underlined indicate the default choice for individual modules. The methods indicated in blue font denote the best-performing selections identified empirically. Source: Searching for Best Practices in Retrieval-Augmented Generation.

A typical RAG workflow includes several intermediate processing steps:

Query classification (determining if the input query requires retrieval)Retrieval (efficiently obtaining relevant documents)Re-ranking (optimizing the order of retrieved documents based on relevance)Re-packing (organizing the retrieved documents into a structured form)Summarization (extracting key information to generate responses and eliminate redundancy)

Implementing RAG also involves deciding how to split documents into chunks, choosing which embeddings to use for semantic representation, selecting… Read the full blog for free on Medium.

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Published via Towards AI