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Customers across all industries are experimenting with generative AI to accelerate and improve business outcomes. Generative AI is used in various use cases, such as content creation, personalization, intelligent assistants, questions and answers, summarization, automation, cost-efficiencies, productivity improvement assistants, customization, innovation, and more. Generative AI solutions often use Retrieval AugmentedContinue Reading

This post is co-written with Vraj Shah and Chaitanya Hari from DoorDash. DoorDash connects consumers with their favorite local businesses in more than 30 countries across the globe. Recently, they faced a significant challenge in handling the high volume of calls from its contractor delivery workers, known as Dashers. WithContinue Reading

Amazon Bedrock Knowledge Bases provides foundation models (FMs) and agents in Amazon Bedrock contextual information from your company’s private data sources for Retrieval Augmented Generation (RAG) to deliver more relevant, accurate, and customized responses. Amazon Bedrock Knowledge Bases offers a fully managed RAG experience. The data sources that can beContinue Reading

Retrieval Augmented Generation (RAG) is a state-of-the-art approach to building question answering systems that combines the strengths of retrieval and generative language models. RAG models retrieve relevant information from a large corpus of text and then use a generative language model to synthesize an answer based on the retrieved information.Continue Reading

AI chatbots and virtual assistants have become increasingly popular in recent years thanks the breakthroughs of large language models (LLMs). Trained on a large volume of datasets, these models incorporate memory components in their architectural design, allowing them to understand and comprehend textual context. Most common use cases for chatbotContinue Reading

Retrieval Augmented Generation (RAG) is a state-of-the-art approach to building question answering systems that combines the strengths of retrieval and foundation models (FMs). RAG models first retrieve relevant information from a large corpus of text and then use a FM to synthesize an answer based on the retrieved information. AnContinue Reading

Information retrieval systems have powered the information age through their ability to crawl and sift through massive amounts of data and quickly return accurate and relevant results. These systems, such as search engines and databases, typically work by indexing on keywords and fields contained in data files. However, much ofContinue Reading