Organizations are continuously seeking ways to use their proprietary knowledge and domain expertise to gain a competitive edge. With the advent of foundation models (FMs) and their remarkable natural language processing capabilities, a new opportunity has emerged to unlock the value of their data assets. As organizations strive to deliverContinue Reading
Twitch, the world’s leading live-streaming platform, has over 105 million average monthly visitors. As part of Amazon, Twitch advertising is handled by the ad sales organization at Amazon. New ad products across diverse markets involve a complex web of announcements, training, and documentation, making it difficult for sales teams toContinue Reading
Generative AI has empowered customers with their own information in unprecedented ways, reshaping interactions across various industries by enabling intuitive and personalized experiences. This transformation is significantly enhanced by Retrieval Augmented Generation (RAG), which is a generative AI pattern where the large language model (LLM) being used references a knowledgeContinue Reading
Retrieval Augmented Generation (RAG) has become a crucial technique for improving the accuracy and relevance of AI-generated responses. The effectiveness of RAG heavily depends on the quality of context provided to the large language model (LLM), which is typically retrieved from vector stores based on user queries. The relevance ofContinue Reading
Today, we are happy to announce the availability of Binary Embeddings for Amazon Titan Text Embeddings V2 in Amazon Bedrock Knowledge Bases and Amazon OpenSearch Serverless. With support for binary embedding in Amazon Bedrock and a binary vector store in OpenSearch Serverless, you can use binary embeddings and binary vectorContinue Reading
In Part 1 of this series, we defined the Retrieval Augmented Generation (RAG) framework to augment large language models (LLMs) with a text-only knowledge base. We gave practical tips, based on hands-on experience with customer use cases, on how to improve text-only RAG solutions, from optimizing the retriever to mitigatingContinue Reading
Large language models (LLMs) are very large deep-learning models that are pre-trained on vast amounts of data. LLMs are incredibly flexible. One model can perform completely different tasks such as answering questions, summarizing documents, translating languages, and completing sentences. LLMs have the potential to revolutionize content creation and the wayContinue Reading
The AWS Generative AI Innovation Center (GenAIIC) is a team of AWS science and strategy experts who have deep knowledge of generative AI. They help AWS customers jumpstart their generative AI journey by building proofs of concept that use generative AI to bring business value. Since the inception of AWSContinue Reading
In the realm of generative artificial intelligence (AI), Retrieval Augmented Generation (RAG) has emerged as a powerful technique, enabling foundation models (FMs) to use external knowledge sources for enhanced text generation. Amazon Bedrock is a fully managed service that offers a choice of high-performing FMs from leading AI companies likeContinue Reading
Evaluating your Retrieval Augmented Generation (RAG) system to make sure it fulfils your business requirements is paramount before deploying it to production environments. However, this requires acquiring a high-quality dataset of real-world question-answer pairs, which can be a daunting task, especially in the early stages of development. This is whereContinue Reading
