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What is Retrieval Augmented Generation (RAG) in Machine Learning?

2025-06-30T03:46:57+00:00

Retrieval-Augmented Generation (RAG) Cheat Sheet Retrieval-Augmented Generation (RAG) is a method that enhances large language models (LLMs) outputs by incorporating information from external, authoritative knowledge sources. Instead of relying solely on pre-trained data, RAG retrieves relevant content at inference time to ground its responses. LLMs (Large Language Models) are trained on massive datasets and use billions of parameters to perform tasks like: Question answering Language translation Text completion RAG extends LLM capabilities to domain-specific or private organizational knowledge without requiring model retraining. It provides a cost-efficient way to improve the relevance, accuracy, and utility of LLM outputs in dynamic or [...]

What is Retrieval Augmented Generation (RAG) in Machine Learning?2025-06-30T03:46:57+00:00

Retrieval-Augmented Generation (RAG) for Foundation Model Customization

2024-12-02T06:01:45+00:00

Artificial Intelligence (AI) has rapidly advanced, pushing the limits of what machines can accomplish. However, one significant challenge remains: ensuring that AI responses are both accurate and contextually relevant while being up-to-date. This is where Retrieval-Augmented Generation (RAG) comes in—a cutting-edge approach that integrates the capabilities of data retrieval with advanced AI generation techniques. In this blog, we will explore the details of RAG, discussing its benefits, applications, and how to implement it using AWS. Understanding Retrieval-Augmented Generation (RAG) RAG (Retrieval-Augmented Generation) incorporates real-time data retrieval into the generative process. Unlike traditional models that depend solely on pre-trained data, RAG [...]

Retrieval-Augmented Generation (RAG) for Foundation Model Customization2024-12-02T06:01:45+00:00

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