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Best practices and lessons for fine-tuning Anthropic’s Claude 3 Haiku on Amazon Bedrock

AWS Machine Learning

In this post, we explore the best practices and lessons learned for fine-tuning Anthropic’s Claude 3 Haiku on Amazon Bedrock. Tools and APIs – For example, when you need to teach Anthropic’s Claude 3 Haiku how to use your APIs well. Sonnet across various tasks.

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Generating value from enterprise data: Best practices for Text2SQL and generative AI

AWS Machine Learning

In this post, we provide an introduction to text to SQL (Text2SQL) and explore use cases, challenges, design patterns, and best practices. Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) via a single API, enabling to easily build and scale Gen AI applications.

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Use the ApplyGuardrail API with long-context inputs and streaming outputs in Amazon Bedrock

AWS Machine Learning

The new ApplyGuardrail API enables you to assess any text using your preconfigured guardrails in Amazon Bedrock, without invoking the FMs. In this post, we demonstrate how to use the ApplyGuardrail API with long-context inputs and streaming outputs. For example, you can now use the API with models hosted on Amazon SageMaker.

APIs 136
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Best practices to build generative AI applications on AWS

AWS Machine Learning

We provide an overview of key generative AI approaches, including prompt engineering, Retrieval Augmented Generation (RAG), and model customization. Building large language models (LLMs) from scratch or customizing pre-trained models requires substantial compute resources, expert data scientists, and months of engineering work.

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Automate emails for task management using Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, and Amazon Bedrock Guardrails

AWS Machine Learning

Amazon Bedrock is a fully managed service that makes foundation models (FMs) from leading AI startups and Amazon Web Services available through an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case.

APIs 118
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Achieve operational excellence with well-architected generative AI solutions using Amazon Bedrock

AWS Machine Learning

Because this is an emerging area, best practices, practical guidance, and design patterns are difficult to find in an easily consumable basis. This integration makes sure enterprises can take advantage of the full power of generative AI while adhering to best practices in operational excellence.

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Knowledge Bases for Amazon Bedrock now supports custom prompts for the RetrieveAndGenerate API and configuration of the maximum number of retrieved results

AWS Machine Learning

In this post, we discuss two new features of Knowledge Bases for Amazon Bedrock specific to the RetrieveAndGenerate API: configuring the maximum number of results and creating custom prompts with a knowledge base prompt template. For best practices on prompt engineering, refer to Prompt engineering guidelines.

APIs 139