article thumbnail

Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

AWS Machine Learning - AI

Knowledge Bases for Amazon Bedrock allows you to build performant and customized Retrieval Augmented Generation (RAG) applications on top of AWS and third-party vector stores using both AWS and third-party models. If you want more control, Knowledge Bases lets you control the chunking strategy through a set of preconfigured options.

article thumbnail

Making OT-IT integration a reality with new data architectures and generative AI

CIO

The company can also unify its knowledge base and promote search and information use that better meets its needs. This data, which is stored on Avanade Insight Discovery, is then processed through Microsoft Copilot, an AI assistant that enables simple and effective search and analysis.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

SAP and Nvidia expand partnership to aid customers with gen AI

CIO

RAG optimizes LLMs by giving them the ability to reference authoritative knowledge bases outside their training data. “There are tons of documents that are not residing in an SAP system,” Herzig said. Artificial Intelligence, Data Architecture, Data Science, Digital Transformation, Generative AI, IT Leadership, Nvidia, SAP

article thumbnail

Automate the insurance claim lifecycle using Agents and Knowledge Bases for Amazon Bedrock

AWS Machine Learning - AI

You can now use Agents for Amazon Bedrock and Knowledge Bases for Amazon Bedrock to configure specialized agents that seamlessly run actions based on natural language input and your organization’s data. The following diagram illustrates the solution architecture. The following are some example prompts: Create a new claim.

article thumbnail

Harness the Power of Pinecone with Cloudera’s New Applied Machine Learning Prototype

Cloudera

The AMP demonstrates how organizations can create a dynamic knowledge base from website data, enhancing the chatbot’s ability to deliver context-rich, accurate responses. An overview of the RAG architecture with a vector database used to minimize hallucinations in the chatbot application.

article thumbnail

Build a contextual chatbot application using Knowledge Bases for Amazon Bedrock

AWS Machine Learning - AI

One way to enable more contextual conversations is by linking the chatbot to internal knowledge bases and information systems. Integrating proprietary enterprise data from internal knowledge bases enables chatbots to contextualize their responses to each user’s individual needs and interests.

article thumbnail

ChatGPT, the rise of generative AI

CIO

Five years later, transformer architecture has evolved to create powerful models such as ChatGPT. ChatGPT’s conversational interface is a distinguished method of accessing its knowledge. This interface paired with increased tokens and an expansive knowledge base with many more parameters, helps ChatGPT to seem quite human-like.