RAG with Amazon Bedrock Knowledge Bases

Overview

Retrieval-Augmented Generation (RAG) is the process of optimizing the output of a large language model, so it references an authoritative knowledge base outside of its training data sources before generating a response. Large Language Models (LLMs) are trained on vast volumes of data and use billions of parameters to generate original output for tasks like answering questions, translating languages, and completing sentences. RAG extends the already powerful capabilities of LLMs to specific domains or an organization's internal knowledge base, all without the need to retrain the model. It is a cost-effective approach to improving LLM output so it remains relevant, accurate, and useful in various contexts.

Technical Details

Amazon Bedrock is a fully-managed service that offers a choice of high-performing foundation models—along with a broad set of capabilities—to build generative AI applications while simplifying development and maintaining privacy and security.

Knowledge base for Amazon Bedrock help you take advantage of Retrieval Augmented Generation (RAG), a popular technique that involves drawing information from a data store to augment the responses generated by Large Language Models (LLMs). When you set up a knowledge base with your data sources, your application can query the knowledge base to return information to answer the query either with direct quotations from sources or with natural responses generated from the query results.

With knowledge bases, you can build applications that are enriched by the context that is received from querying a knowledge base. It enables a faster time to market by abstracting from the heavy lifting of building pipelines and providing you an out-of-the-box RAG solution to reduce the build time for your application. Adding a knowledge base also increases cost-effectiveness by removing the need to continually train your model to be able to leverage your private data.