This April, we announced Amazon Bedrock as part of a set of new tools for building with generative AI on AWS. Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies, including AI21 Labs, Anthropic, Cohere, Stability AI, and Amazon, along with a broad set of capabilities to build generative AI applications, simplifying the development while maintaining privacy and security.

Today, I’m happy to announce that Amazon Bedrock is now generally available! I’m also excited to share that Meta’s Llama 2 13B and 70B parameter models will soon be available on Amazon Bedrock.

Amazon Bedrock’s comprehensive capabilities help you experiment with a variety of top FMs, customize them privately with your data using techniques such as fine-tuning and retrieval-augmented generation (RAG), and create managed agents that perform complex business tasks—all without writing any code. Check out my previous posts to learn more about agents for Amazon Bedrock and how to connect FMs to your company’s data sources.

Note that some capabilities, such as agents for Amazon Bedrock, including knowledge bases, continue to be available in preview. I’ll share more details on what capabilities continue to be available in preview towards the end of this blog post.

Since Amazon Bedrock is serverless, you don’t have to manage any infrastructure, and you can securely integrate and deploy generative AI capabilities into your applications using the AWS services you are already familiar with.

Amazon Bedrock is integrated with Amazon CloudWatch and AWS CloudTrail to support your monitoring and governance needs. You can use CloudWatch to track usage metrics and build customized dashboards for audit purposes. With CloudTrail, you can monitor API activity and troubleshoot issues as you integrate other systems into your generative AI applications. Amazon Bedrock also allows you to build applications that are in compliance with the GDPR and you can use Amazon Bedrock to run sensitive workloads regulated under the U.S. Health Insurance Portability and Accountability Act (HIPAA).

Get Started with Amazon Bedrock
You can access available FMs in Amazon Bedrock through the AWS Management Console, AWS SDKs, and open-source frameworks such as LangChain.

In the Amazon Bedrock console, you can browse FMs and explore and load example use cases and prompts for each model. First, you need to enable access to the models. In the console, select Model access in the left navigation pane and enable the models you would like to access. Once model access is enabled, you can try out different models and inference configuration settings to find a model that fits your use case.

For example, here’s a contract entity extraction use case example using Cohere’s Command model:

The example shows a prompt with a sample response, the inference configuration parameter settings for the example, and the API request that runs the example. If you select Open in Playground, you can explore the model and use case further in an interactive console experience.

Amazon Bedrock offers chat, text, and image model playgrounds. In the chat playground, you can experiment with various FMs using a conversational chat interface. The following example uses Anthropic’s Claude model:

As you evaluate different models, you should try various prompt engineering techniques and inference configuration parameters. Prompt engineering is a new and exciting skill focused on how to better understand and apply FMs to your tasks and use cases. Effective prompt engineering is about crafting the perfect query to get the most out of FMs and obtain proper and precise responses. In general, prompts should be simple, straightforward, and avoid ambiguity. You can also provide examples in the prompt or encourage the model to reason through more complex tasks.

Inference configuration parameters influence the response generated by the model. Parameters such as Temperature, Top P, and Top K give you control over the randomness and diversity, and Maximum Length or Max Tokens control the length of model responses. Note that each model exposes a different but often overlapping set of inference parameters. These parameters are either named the same between models or similar enough to reason through when you try out different models.

We discuss effective prompt engineering techniques and inference configuration parameters in more detail in week 1 of the Generative AI with Large Language Models on-demand course, developed by AWS in collaboration with DeepLearning.AI. You can als