Characterising and Analysing Retrieval-Augmented Generation Techniques

Emmanuel Igbekele; Olawande Daramola

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arXiv · 2610.23226

Characterising and Analysing Retrieval-Augmented Generation Techniques

Emmanuel Igbekele·Olawande Daramola

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Abstract

The advent of Large Language Models (LLMs) across many application domains has increased the need for Retrieval-Augmented Generation (RAG) to reduce hallucinations and improve LLM performance. Thus far, several RAG techniques with impressive features and capabilities have been proposed in the literature, making it a herculean task to select the most appropriate RAG techniques for specific problem scenarios. Thus, we propose a conceptual framework comprising main dimensions and sub-dimensions for characterising (defining "what it is") and analysing (defining "how well it does") existing RAG techniques. The conceptual framework was developed through a qualitative analysis of the attributes of RAG techniques reported in the literature. It can serve as a pre-empirical filtering mechanism for researchers and practitioners in selecting the highest-fit RAG candidates that meet specific user and task requirements before empirical benchmarking of RAG techniques, which can be computationally and financially expensive.

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Emmanuel Igbekele, Olawande Daramola. 2026-10-16. Characterising and Analysing Retrieval-Augmented Generation Techniques. https://arxiv.org/abs/2610.23226

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