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Build generative AI chatbots using prompt engineering with Amazon Redshift and Amazon Bedrock

AWS Machine Learning

Amazon Bedrock offers a choice of high-performing foundation models from leading AI companies, including AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon, via a single API. Prompt engineering makes generative AI applications more efficient and effective.

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Create a multimodal chatbot tailored to your unique dataset with Amazon Bedrock FMs

AWS Machine Learning

With recent advances in large language models (LLMs), a wide array of businesses are building new chatbot applications, either to help their external customers or to support internal teams. Also, note the physical ID of the API Gateway connection, which should look something like zxpdjtklw2 , as shown in the following screenshot.

Chatbots 122
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Build a video insights and summarization engine using generative AI with Amazon Bedrock

AWS Machine Learning

This post presents a solution where you can upload a recording of your meeting (a feature available in most modern digital communication services such as Amazon Chime ) to a centralized video insights and summarization engine. This post provides guidance on how you can create a video insights and summarization engine using AWS AI/ML services.

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Integrate dynamic web content in your generative AI application using a web search API and Amazon Bedrock Agents

AWS Machine Learning

Amazon Bedrock agents use LLMs to break down tasks, interact dynamically with users, run actions through API calls, and augment knowledge using Amazon Bedrock Knowledge Bases. In this post, we demonstrate how to use Amazon Bedrock Agents with a web search API to integrate dynamic web content in your generative AI application.

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Automate chatbot for document and data retrieval using Agents and Knowledge Bases for Amazon Bedrock

AWS Machine Learning

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.

Chatbots 122
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Build a contextual chatbot for financial services using Amazon SageMaker JumpStart, Llama 2 and Amazon OpenSearch Serverless with Vector Engine

AWS Machine Learning

In this post, we explore building a contextual chatbot for financial services organizations using a RAG architecture with the Llama 2 foundation model and the Hugging Face GPTJ-6B-FP16 embeddings model, both available in SageMaker JumpStart. Lewis et al. The following diagram shows the conceptual flow of using RAG with LLMs.

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Build a contextual chatbot application using Knowledge Bases for Amazon Bedrock

AWS Machine Learning

Modern chatbots can serve as digital agents, providing a new avenue for delivering 24/7 customer service and support across many industries. Chatbots also offer valuable data-driven insights into customer behavior while scaling effortlessly as the user base grows; therefore, they present a cost-effective solution for engaging customers.

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