Architecture to AWS CloudFormation code using Anthropic’s Claude 3 on Amazon Bedrock

The Anthropic’s Claude 3 family of models, available on Amazon Bedrock , offers multimodal capabilities that enable the processing of images and text. This capability opens up innovative avenues for image understanding, wherein Anthropic’s Claude 3 models can analyze visual information in conjunction with textual data, facilitating more comprehensive and contextual interpretations. By taking advantage of its multimodal prowess, we can ask the model questions like “What objects are in the image, and how are they relatively positioned to each other?” We can also gain an understanding of data presented in charts and graphs by asking questions related to business intelligence (BI) tasks, such as “What is the sales trend for 2023 for company A in the enterprise market?” These are just some examples of the additional richness Anthropic’s Claude 3 brings to generative artificial intelligence (AI) interactions.

Architecting specific AWS Cloud solutions involves creating diagrams that show relationships and interactions between different services. Instead of building the code manually, you can use Anthropic’s Claude 3’s image analysis capabilities to generate AWS CloudFormation templates by passing an architecture diagram as input.

In this post, we explore some ways you can use Anthropic’s Claude 3 Sonnet’s vision capabilities to accelerate the process of moving from architecture to the prototype stage of a solution.

Use cases for architecture to code

The following are relevant use cases for this solution:

• Converting whiteboarding sessions to AWS infrastructure – To quickly prototype your designs, you can take the architecture diagrams created during whiteboarding sessions and generate the first draft of a CloudFormation template. You can also iterate over the CloudFormation template to develop a well-architected solution that meets all your requirements.
• Fast deployment of architecture diagrams – You can generate boilerplate CloudFormation templates by using architecture diagrams you find on the web. This allows you to experiment quickly with new designs.
• Streamlined AWS infrastructure design through collaborative diagramming – You might draw architecture diagrams on a diagramming tool during an all-hands meeting. These raw diagrams can generate boilerplate CloudFormation templates, quickly leading to actionable steps while speeding up collaboration and increasing meeting value.

Solution overview

To demonstrate the solution, we use Streamlit to provide an interface for diagrams and prompts. Amazon Bedrock invokes the Anthropic’s Claude 3 Sonnet model, which provides multimodal capabilities. AWS Fargate is the compute engine for web application. The following diagram illustrates the step-by-step process.

The workflow consists of the following steps:

1. The user uploads an architecture image (JPEG or PNG) on the Streamlit application, invoking the Amazon Bedrock API to generate a step-by-step explanation of the architecture using the Anthropic’s Claude 3 Sonnet model.
2. The Anthropic’s Claude 3 Sonnet model is invoked using a step-by-step explanation and few-shot learning examples to generate the initial CloudFormation code. The few-shot learning example consists of three CloudFormation templates; this helps the model understand writing practices associated with CloudFormation code.
3. The user manually provides instructions using the chat interface to update the initial CloudFormation code.

*Steps 1 and 2 are executed once when architecture diagram is uploaded. To trigger changes to the AWS CloudFormation code (step 3) provide update instructions from the Streamlit app

The CloudFormation templates generated by the web application are intended for inspiration purposes and not for production-level applications. It is the responsibility of a developer to test and verify the CloudFormation template according to security guidelines.

Few-shot Prompting

To help Anthropic’s Claude 3 Sonnet understand the practices of writing CloudFormation code, we use few-shot prompting by providing three CloudFormation templates as reference examples in the prompt. Exposing Anthropic’s Claude 3 Sonnet to multiple CloudFormation templates will allow it to analyze and learn from the structure, resource definitions, parameter configurations, and other essential elements consistently implemented across your organization’s templates. This enables Anthropic’s Claude 3 Sonnet to grasp your team’s coding conventions, naming conventions, and organizational patterns when generating CloudFormation templates. The following examples used for few-shot learning can be found in the GitHub repo .

Furthermore, Anthropic’s Claude 3 Sonnet can observe how different resources and services are configured and integrated within the CloudFormation templates through few-shot prompting. It will gain insights into how to automate the deployment and management of various AWS resources, such as Amazon Simple Storage Service (Amazon S3) , AWS Lambda , Amazon DynamoDB , and AWS Step Functions .

Inference parameters are preset, but they can be changed from the web application if desired. We recommend experimenting with various combinations of these parameters. By default, we set the temperature to zero to reduce the variability of outputs and create focused, syntactically correct code.