Author(s): Rashmi
Originally published on Towards AI.
The Complete Guide to RAG Systems
Retrieval-augmented generation (RAG) has revolutionized building intelligent systems by combining the power of large language models with external knowledge retrieval. As organizations grapple with hallucinations, outdated information, and domain-specific knowledge gaps in LLM, RAG has emerged as the de facto solution for grounding AI responses in factual, up-to-date information.
This article provides an in-depth exploration of recovery-augmented generation (RAG) systems, describing their function, architecture, and various implementations. It discusses the challenges faced by large language models, such as obfuscation and knowledge gaps, before presenting RAG as a viable solution. This piece breaks down the core components of RAG systems, including document processing pipelines and retrieval mechanisms, and highlights the importance of embedding models and vector databases. Additionally, it examines the benefits of RAGs for improved factual accuracy, source attribution, and dynamic knowledge access, providing insight into emerging architectures and potential future trends.
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Published via Towards AI
