Amazon Bedrock provides access to pretrained, powerful AI models from leading providers and a suite of tools to customize, deploy, and integrate these models into diverse workflows. Let’s explore Amazon Bedrock’s standout features, covering how it enables businesses to build scalable AI solutions more easily, securely, and cost-effectively.

Knowledge bases

The knowledge base is a repository that stores up-to-date and proprietary information for easy access by generative AI models. It empowers AI applications to deliver more relevant, accurate, and customized responses on demand. It allows for a quicker time to market by relieving you of the burden of pipeline construction and offering you an end-to-end RAGRetrieval Augmented Generation (RAG) is a widely used method that adds private data from a data store (vector database) to the responses produced by large language models (LLMs). solution to shorten the application’s build time. You don’t need to continually train your model by adding a knowledge base, increasing cost-effectiveness.

How knowledge bases work

Foundation models (FMs) are trained on massive datasets, but these datasets are usually general-purpose and outdated. To allow FMs to grasp context, similarity, and relevance between different data points, you can leverage knowledge bases in Bedrock. This process is invaluable in search, recommendation, personalization, and various NLP applications, where understanding the relationship between terms, phrases, or user preferences is critical.

Get hands-on with 1400+ tech skills courses.