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Evolution of RAGs: Naive RAG, Advanced RAG, and Modular RAG Architectures
Large language models (LLMs) like ChatGPT, Bard, and Claude have transformed AI with their ability to generate text for various tasks. However, they face challenges like outdated knowledge and non-transparent reasoning processes. Retrieval-augmented generation (RAG) has emerged as a solution by incorporating knowledge from external databases, improving accuracy and credibility for knowledge-intensive tasks.
How RAG Works
RAG enhances LLMs by retrieving relevant document chunks from external knowledge bases, reducing the generation of factually incorrect content. It combines LLMs with embedding models and vector databases to process user queries and retrieve related data for generating responses.
RAG Research Paradigm
RAG is categorized into three stages: Naive RAG, Advanced RAG, and Modular RAG, each addressing limitations of the previous stage to enhance retrieval quality and adaptability.
Naive RAG
Naive RAG follows a traditional “Retrieve-Read” framework, but encounters challenges like retrieval precision, generation issues, and integration difficulties.
Advanced RAG
Advanced RAG introduces improvements in indexing techniques, query optimization, and retrieval strategies to enhance retrieval quality.
Modular RAG
Modular RAG offers enhanced adaptability and versatility by incorporating diverse strategies and introducing new specialized components to enhance retrieval and processing capabilities.
Practical AI Solutions
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