Category: Amazon Bedrock

While Generative AI can create highly realistic content, including text, images, and videos, it can also generate outputs that appear plausible but are verifiably incorrect. Incorporating human judgment is crucial, especially in complex and high-risk decision-making scenarios. This involves building a human-in-the-loop process where humans play an active role in decision making alongside the AI system. In this blog post, you will learn about prompt chaining, how to break a complex task into multiple tasks to use prompt chaining with an LLM in a specific order, and how to involve a human to review the response generated by the LLM.

Crafting new questions for exams and quizzes can be tedious and time-consuming for educators. The time required varies based on factors like subject matter, question types, experience level, and class level. Multiple-choice questions require substantial time to generate quality distractors and ensure a single unambiguous answer, and composing effective true-false questions demands careful effort to […]

In this post, we provide an overview of the state-of-the-art embedding models by Voyage AI and show a RAG implementation with Voyage AI’s text embedding model on Amazon SageMaker Jumpstart, Anthropic’s Claude 3 model on Amazon Bedrock, and Amazon OpenSearch Service. Voyage AI’s embedding models are the preferred embedding models for Anthropic. In addition to general-purpose embedding models, Voyage AI offers domain-specific embedding models that are tuned to a particular domain.

Amazon Titan Text Premier, the latest addition to the Amazon Titan family of large language models (LLMs), is now generally available in Amazon Bedrock. Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading artificial intelligence (AI) companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and […]

In this post, we provide a comparison of results obtained by two such techniques: zero-shot and few-shot prompting. We also explore the utility of the RAG prompt engineering technique as it applies to the task of summarization.
Evaluating LLMs is an undervalued part of the machine learning (ML) pipeline.

This post is co-written with Tom Famularo, Abhay Shah and Nicolette Kontor from Verisk. Verisk (Nasdaq: VRSK) is a leading data analytics and technology partner for the global insurance industry. Through advanced analytics, software, research, and industry expertise across over 20 countries, Verisk helps build resilience for individuals, communities, and businesses. The company is committed […]

This post is co-written with Tim Camara, Senior Product Manager at Veritone. Veritone is an artificial intelligence (AI) company based in Irvine, California. Founded in 2014, Veritone empowers people with AI-powered software and solutions for various applications, including media processing, analytics, advertising, and more. It offers solutions for media transcription, facial recognition, content summarization, object […]

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 […]