AWS Machine Learning Blog

Category: Amazon Bedrock

Orchestrate an intelligent document processing workflow using tools in Amazon Bedrock

This intelligent document processing solution uses Amazon Bedrock FMs to orchestrate a sophisticated workflow for handling multi-page healthcare documents with mixed content types. The solution uses the FM’s tool use capabilities, accessed through the Amazon Bedrock Converse API. This enables the FMs to not just process text, but to actively engage with various external tools and APIs to perform complex document analysis tasks.

Reducing hallucinations in LLM agents with a verified semantic cache using Amazon Bedrock Knowledge Bases

This post introduces a solution to reduce hallucinations in Large Language Models (LLMs) by implementing a verified semantic cache using Amazon Bedrock Knowledge Bases, which checks if user questions match curated and verified responses before generating new answers. The solution combines the flexibility of LLMs with reliable, verified answers to improve response accuracy, reduce latency, and lower costs while preventing potential misinformation in critical domains such as healthcare, finance, and legal services.

Generate synthetic counterparty (CR) risk data with generative AI using Amazon Bedrock LLMs and RAG

In this post, we explore how you can use LLMs with advanced Retrieval Augmented Generation (RAG) to generate high-quality synthetic data for a finance domain use case. You can use the same technique for synthetic data for other business domain use cases as well. For this post, we demonstrate how to generate counterparty risk (CR) data, which would be beneficial for over-the-counter (OTC) derivatives that are traded directly between two parties, without going through a formal exchange.

Turbocharging premium audit capabilities with the power of generative AI: Verisk’s journey toward a sophisticated conversational chat platform to enhance customer support

Verisk’s Premium Audit Advisory Service is the leading source of technical information and training for premium auditors and underwriters. In this post, we describe the development of the customer support process in PAAS, incorporating generative AI, the data, the architecture, and the evaluation of the results. Conversational AI assistants are rapidly transforming customer and employee support.

How Formula 1® uses generative AI to accelerate race-day issue resolution

In this post, we explain how F1 and AWS have developed a root cause analysis (RCA) assistant powered by Amazon Bedrock to reduce manual intervention and accelerate the resolution of recurrent operational issues during races from weeks to minutes. The RCA assistant enables the F1 team to spend more time on innovation and improving its services, ultimately delivering an exceptional experience for fans and partners. The successful collaboration between F1 and AWS showcases the transformative potential of generative AI in empowering teams to accomplish more in less time.

Use language embeddings for zero-shot classification and semantic search with Amazon Bedrock

In this post, we explore what language embeddings are and how they can be used to enhance your application. We show how, by using the properties of embeddings, we can implement a real-time zero-shot classifier and can add powerful features such as semantic search.

Fine-tune LLMs with synthetic data for context-based Q&A using Amazon Bedrock

In this post, we explore how to use Amazon Bedrock to generate synthetic training data to fine-tune an LLM. Additionally, we provide concrete evaluation results that showcase the power of synthetic data in fine-tuning when data is scarce.

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LLM-as-a-judge on Amazon Bedrock Model Evaluation

This blog post explores LLM-as-a-judge on Amazon Bedrock Model Evaluation, providing comprehensive guidance on feature setup, evaluating job initiation through both the console and Python SDK and APIs, and demonstrating how this innovative evaluation feature can enhance generative AI applications across multiple metric categories including quality, user experience, instruction following, and safety.