A Taxonomy of Retrieval Augmented Generation

Author(s): Abhinav Kimothi

Originally published on Towards AI.

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Eight Themes of RAG Taxonomy (Source: Image by Author)

Retrieval Augmented Generation, or RAG, stands as a pivotal technique shaping the landscape of the applied generative AI. A novel concept introduced by Lewis et al in their seminal paper Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, RAG has swiftly emerged as a cornerstone, enhancing reliability and trustworthiness in the outputs from Large Language Models (LLMs).

In 2024, RAG is one of the most widely used techniques in generative AI applications.

As per Databricks, at least 60% of LLM applications utilise some form of RAG.

RAG’s acceptance is also propelled by the simplicity of the concept. Simply put, a RAG system searches for information from a knowledge base and sends it along with the query to the LLM for the response.

Retrieval Augmented Generation enhances the reliability and the trustworthiness in LLM responses (Source: Image by Author)

RAG today encompasses a wide array of techniques, models, and approaches. It can get a little overwhelming for newcomers. As RAG continues to evolve it’s crucial to create a shared language framework for researchers, practitioners, developers and business leaders. This taxonomy is an attempt to clarify the components of RAG,… Read the full blog for free on Medium.

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Published via Towards AI