Category: Amazon Bedrock

Embeddings are integral to various natural language processing (NLP) applications, and their quality is crucial for optimal performance. They are commonly used in knowledge bases to represent textual data as dense vectors, enabling efficient similarity search and retrieval. In Retrieval Augmented Generation (RAG), embeddings are used to retrieve relevant passages from a corpus to provide […]

Imagine harnessing the power of advanced language models to understand and respond to your customers’ inquiries. Amazon Bedrock, a fully managed service providing access to such models, makes this possible. Fine-tuning large language models (LLMs) on domain-specific data supercharges tasks like answering product questions or generating relevant content. In this post, we show how Amazon […]

Numerous customers face challenges in managing diverse data sources and seek a chatbot solution capable of orchestrating these sources to offer comprehensive answers. This post presents a solution for developing a chatbot capable of answering queries from both documentation and databases, with straightforward deployment. Amazon Bedrock is a fully managed service that offers a choice […]

In the fast-paced world of customer service, efficiency and accuracy are paramount. After each call, contact center agents often spend up to a third of the total call time summarizing the customer conversation. Additionally, manual summarization can lead to inconsistencies in the style and level of detail due to varying interpretations of note-taking guidelines. This […]

At AWS re:Invent 2023, we announced the general availability of Amazon Bedrock Knowledge Bases. With Amazon Bedrock Knowledge Bases, you can securely connect foundation models (FMs) in Amazon Bedrock to your company data for fully managed Retrieval Augmented Generation (RAG). In previous posts, we covered new capabilities like hybrid search support, metadata filtering to improve […]

Conversational artificial intelligence (AI) assistants are engineered to provide precise, real-time responses through intelligent routing of queries to the most suitable AI functions. With AWS generative AI services like Amazon Bedrock, developers can create systems that expertly manage and respond to user requests. Amazon Bedrock is a fully managed service that offers a choice of […]

We introduced Amazon Bedrock to the world a little over a year ago, delivering an entirely new way to build generative artificial intelligence (AI) applications. With the broadest selection of first- and third-party foundation models (FMs) as well as user-friendly capabilities, Amazon Bedrock is the fastest and easiest way to build and scale secure generative […]

This post explores the new enterprise-grade features for Amazon Bedrock Knowledge Bases and how they align with the AWS Well-Architected Framework. With Amazon Bedrock Knowledge Bases, you can quickly build applications using Retrieval Augmented Generation (RAG) for use cases like question answering, contextual chatbots, and personalized search.

Organizations are facing ever-increasing requirements for sustainability goals alongside environmental, social, and governance (ESG) practices. A Gartner, Inc. survey revealed that 87 percent of business leaders expect to increase their organization’s investment in sustainability over the next years. This post serves as a starting point for any executive seeking to navigate the intersection of generative […]

In Part 1 of this series, we presented a solution that used the Amazon Titan Multimodal Embeddings model to convert individual slides from a slide deck into embeddings. We stored the embeddings in a vector database and then used the Large Language-and-Vision Assistant (LLaVA 1.5-7b) model to generate text responses to user questions based on […]