Finishing touches

blog/2024-10-15-introduction-to-rag/index.mdx

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 The beauty of RAG lies in the fact that the weights of the underlying generative model don’t need to be updated, which can be costly and time-consuming. RAG allows models to access external data dynamically, improving accuracy without costly retraining. This makes it a practical solution for applications needing real-time information. In the next section, we’ll dive deeper into the architecture of RAG and how its components work together to create a powerful retrieval-augmented system.
 
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 In this article, we introduced you to RAG, a framework that leverages task-specific external knowledge to improve the performance of applications powered by generative models. We learned about the different components of RAG pipelines, including external knowledge sources, prompt templates, and generative models as well as how they work together in retrieval, augmentation, and generation. We also discussed popular RAG use cases and frameworks for implementation, such as LangChain, LlamaIndex, and DSPy. Finally, we touched on some specialized RAG techniques, including advanced RAG methods, agentic RAG, and graph RAG as well as methods for evaluating RAG pipelines.