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Advanced RAG Techniques: What They Are & How to Use Them

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Retrieval-Augmented Generation (RAG) has become a mainstream approach for working with large language models (LLMs) since its introduction in early research. At its core, RAG gathers knowledge from various sources and generates answers using a language model. However, with basic RAG, also known as Naive RAG, you may encounter challenges in obtaining accurate results for complex queries and face slow response times and higher costs when dealing with large datasets. To address these challenges, researchers have developed several advanced RAG techniques. This article provides an overview of these advanced methods to help you achieve better results when Naive RAG falls short. Every RAG application can be broken down into two phases: retrieval and generation. First, RAG retrieves relevant documents or knowledge snippets from external sources, such as knowledge graphs or vector stores, using search and indexing techniques. This retrieved data is then fed into a language model, which generate

Advanced RAG Techniques: What They Are & How to Use Them Back to all posts Advanced RAG Techniques: What They Are & How to Use Them Guy Korland Date Published: August 14, 2024 Date Updated: April 2, 2025 Retrieval-Augmented Generation (RAG) has become a mainstream approach for working with large language models (LLMs) since its introduction in early research . At its core, RAG gathers knowledge from various sources and generates answers using a language model. However, with basic RAG, also known as Naive RAG, you may encounter challenges in obtaining accurate results for complex queries a

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