Amazon Titan Text Premier

An advanced language model designed to build generative AI applications based on Retrieval Augmented Generation (RAG) and agents

PremiumNewProductProductivityArtificial IntelligenceNatural Language Processing

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Amazon Titan Text Premier is a new member of the Amazon Titan model series, designed specifically for text-based enterprise applications. It supports customizable fine-tuning to adapt to specific domains, organizations, brand styles, and use cases. The model is provided in Amazon Bedrock, featuring a maximum context length of 32K tokens, particularly suited for English tasks, and integrates responsible AI practices.

Optimize retrieval augmented generation (RAG) and agent-based applications
Integrate with Amazon Bedrock's knowledge bases to achieve high-quality RAG
Automate tasks across systems and data sources through integration with Amazon Bedrock agents
Customize fine-tuning to improve model accuracy and create unique user experiences
Integrate secure
reliable
and trustworthy practices
Showcase impressive performance in public benchmarks and suitable for a wide range of enterprise applications

Corporate users: Build generative AI applications tailored to their data and business processes
Developers: Rapidly develop and deploy AI solutions by utilizing the model's powerful functionalities
Data Scientists: Optimize the performance of specific tasks by fine-tuning the model

Interactive AI assistant that creates summaries from unstructured data such as emails using Titan Text Premier
Utilize the model to extract information from company systems and data sources to generate more meaningful product summaries
Automate multi-step tasks such as retail order management or insurance claim processing through integration with Titan Text Premier and agents

Step 1: Log in to the Amazon Bedrock console and select model access
Step 2: On the model access overview page
enable access to Amazon Titan Text Premier
Step 3:召唤模型 Using AWS CLI or AWS SDKs to call the model and provide example prompts
Step 4: Adjust inference parameters such as temperature (temperature) and topP to control the randomness and diversity of responses as needed

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Amazon Titan Text Premier Visit Over Time

Monthly Visits

60001826

Bounce Rate

30.85%

Page per Visit

13.3

Visit Duration

00:11:13

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