RAG
Explore how Retrieval-Augmented Generation (RAG) integrates data retrieval with generative AI using Amazon Bedrock. Learn the two-phase process involving document retrieval and context-aware response generation to produce accurate and relevant outputs. Understand best practices for implementing RAG in real-world scenarios like customer support assistants, using AWS services such as Amazon Kendra and DynamoDB to optimize information retrieval and response quality.
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Retrieval-augmented generation (RAG) is a model that combines a retrieval component with a generative model. This means that when an RAG model is prompted to generate text or answer a question, it first retrieves relevant information from a vast database. It then uses this information to provide responses that are created by using specific, real-world data rather than relying solely on pretrained knowledge.
This dynamic approach allows RAG models to produce more accurate, timely, and contextually appropriate outputs, significantly reducing the occurrence of errors and hallucinations that are typical of traditional models.
There are two main phases of RAG:
Retrieval phase: ...