Streamline generative AI development in Amazon Bedrock with Prompt Management and Prompt Flows (preview)

Today, we’re excited to introduce two powerful new features for Amazon Bedrock: Prompt Management and Prompt Flows, in public preview. These features are designed to accelerate the development, testing, and deployment of generative artificial intelligence (AI) applications, enabling developers and business users to create more efficient and effective solutions that are easier to maintain. You can use the Prompt Management and Flows features graphically on the Amazon Bedrock console or Amazon Bedrock Studio, or programmatically through the Amazon Bedrock SDK APIs.

As the adoption of generative AI continues to grow, many organizations face challenges in efficiently developing and managing prompts. Also, modern applications often require chaining or routing logics that add complexity to the development. With the Prompt Management and Flows features, Amazon Bedrock addresses these pain points by providing intuitive tools for designing and storing prompts, creating complex workflows, and advancing collaboration among team members.

Before introducing the details of the new capabilities, let’s review how prompts are typically developed, managed, and used in a generative AI application.

The prompt lifecycle

Developing effective prompts for generative AI applications is an iterative process that requires careful design, testing, and refinement. Understanding this lifecycle is crucial for creating high-quality, reliable AI-powered solutions. Let’s explore the key stages of a typical prompting lifecycle:

Prompt design – This initial stage involves crafting prompts that effectively communicate the desired task or query to the foundation model. Prompts are often built as prompt templates that contain variables, dynamic context, or other content to be provided at inference time. Good prompt design considers factors such as clarity, specificity, and context to elicit the most relevant and accurate responses.
Testing and evaluation – After they’re designed, prompts or prompt templates are tested with various inputs to assess their performance and robustness. This stage often involves comparing multiple variations to identify the most effective formulations.
Refinement – Based on the testing results, prompts are iteratively refined to improve their effectiveness. This often involves adjusting wording, adding or removing context, or modifying the structure of the prompt.
Versioning and cataloging – As prompts are developed and refined, it’s crucial to maintain versions and organize them in a prompt catalog. This allows teams to track changes, compare performance across versions, and access proven prompts for reuse.
Deployment – After prompts have been optimized, they can be deployed as part of a generative AI application. This involves integrating the prompt into a larger system or workflow.
Monitoring and iteration – After deployment, teams continually monitor the performance of prompts in live applications and iterate to maintain or improve their effectiveness.

Throughout this lifecycle, the prompt design and prompt catalog play critical roles. A well-designed prompt significantly enhances the quality and relevance of AI-generated responses. A comprehensive prompt catalog is a valuable resource for developers, enabling them to use proven prompts and best practices across projects, saving both time and money.

For more complex generative AI applications, developers often employ patterns such as prompt chaining or prompt routing. These approaches allow for the definition of more sophisticated logic and dynamic workflows, often called prompt flows.

Prompt chaining uses the output of one prompt as input for another, creating a sequence of interactions with the foundation model (FM) to accomplish more complex tasks. For example, a customer service chatbot could initially use an FM to extract key information about a customer and their issue, then pass the details as input for calling a function to open a support ticket. The following diagram illustrates this workflow.

Prompt routing refers to the process of dynamically selecting and applying different prompts based on certain conditions or the nature of the input, allowing for more flexible and context-aware AI applications. For example, a user request to a banking assistant could dynamically decide if the answer can be best found with Retrieval Augmented Generation (RAG) when asked about the available credit cards details, or calling a function for running a query when the user asks about their account balance. The following diagram illustrates this workflow.

Combining these two patterns is common in modern generative AI application development. By understanding and optimizing each stage of the prompting lifecycle and using techniques like chaining and routing, you can create more powerful, efficient, and effective generative AI solutions.

Let’s dive into the new features in Amazon Bedrock and explore how they can help you transform your generative AI development process.

Prompt management: Optimize your AI interactions

The Prompt Management feature streamlines the creation, evaluation, deployment, and sharing of prompts. This feature helps developers and business users obtain the best responses from FMs for their specific use cases.

Key benefits of Prompt Management include the following:

Rapid prompt creation and iteration – Create your prompts and variations with the built-in prompt builder on the Amazon Bedrock console or with the CreatePrompt Incorporate dynamic information using inputs for building your prompt templates.
Seamless testing and deployment – Quickly test individual prompts, set variables and their test values. Create prompt versions stored in the built-in prompt library for cataloging and management using the Amazon Bedrock console or the GetPrompt, ListPrompts, and CreatePromptVersion
Collaborative prompt development – Use your prompts and prompt templates in flows or Amazon Bedrock Studio. Prompt management enables team members to collaborate on prompt creation, evaluation, and deployment, improving efficiencies in the development process.

There are no prerequisites for using the Prompt Management feature beyond access to the Amazon Bedrock console. For information on AWS Regions and models supported, refer to Prompt management in Amazon Bedrock. If you don’t currently have access to the Amazon Bedrock console, refer to Set up Amazon Bedrock.

To get started with the Prompt Management feature on the Amazon Bedrock console, complete the following steps:

On the Amazon Bedrock console, under Builder tools in the navigation pane, choose Prompt management.

Create a new prompt or select an existing one from the prompt library.

Use the prompt builder to select a model, set parameters, and write the prompt content.

Configure variables for creating prompt templates and test your prompts dynamically.

Create and manage prompt versions for using in your generative AI flows.

Prompt flows: Visualize and accelerate Your AI workflows

The Amazon Bedrock Flows feature introduces a visual builder that simplifies the creation of complex generative AI workflows. This feature allows you to link multiple FMs, prompts, and other AWS services, reducing development time and effort.

Key benefits of prompt flows include:

Intuitive visual builder – Drag and drop components to create a flow, linking prompts with other prompts, AI services, knowledge bases, and business logic. This visual approach helps eliminate the need for extensive coding and provides a comprehensive overview of your application’s structure. Alternatively, you can use the CreateFlow API for a programmatic creation of flows that help you automate processes and development pipelines.
Rapid testing and deployment – Test your flows directly on the Amazon Bedrock console for faster iteration or using the InvokeFlow At any time, you can snapshot the flow for integration into your generative AI application. The flow is surfaced through an Agents for Amazon Bedrock runtime endpoint. You can create flow versions on the Amazon Bedrock console or with the CreateFlowVersion API. Creating an alias on the Amazon Bedrock console or with the CreateFlowAlias API enables straightforward rollbacks and A/B testing between different versions of the flow without impacting your service or development pipelines.
Manage and templatize – Accelerate your development with flow templates for repeated common use cases. You can manage your flows on the Amazon Bedrock console or with the GetFlow and ListFlows

Before you get started in your account, refer to How Flows for Amazon Bedrock works for details on the permissions required and quotas. When you’re ready, complete the following steps to get started with flows on the Amazon Bedrock console:

On the Amazon Bedrock console, under Builder tools in the navigation pane, choose Flows.
Create a flow by providing a name, description, and AWS Identity and Access Management (IAM) role.
Access the visual builder in the working draft of your flow.
Drag and drop individual components or nodes, including prompt templates from your prompt catalog, and link them together. You can edit the properties of each node and use other elements available in Amazon Bedrock.
Use the available nodes to implement conditions, code hooks with AWS Lambda functions, or integrations with AI services such as Amazon Lex, among many other options to be added soon. You can chain or route steps to define your own logic and processing outputs.
Test your prompt flows dynamically and set up your outputs for deploying your generative AI applications.

In our example, we create a flow for dynamically routing the user question to either query a knowledge base in Amazon Bedrock or respond directly from the LLM. We can now invoke this flow from our application frontend.

Example use case: Optimizing ecommerce customer service chatbots

To illustrate the power of these new features, let’s consider Octank, a fictional large ecommerce company facing challenges to efficiently create, test, and deploy AI-powered customer service chatbots for different product categories. This resulted in inconsistent performance and slow iteration cycles.

In the following notebook, we provide a guided example that you can follow to get started with Prompt Management and Prompt Flows programmatically.

Using prompt management and flows in Amazon Bedrock, Octank’s development and prompt engineering teams can now accomplish the following:

Create visual and programmatic workflows for each product category chatbot, incorporating different FMs and AI services as needed
Rapidly prototype and test prompt variations for each chatbot, optimizing for accuracy and relevance
Collaborate across teams to refine prompts and share best practices
Deploy and A/B test different chatbot versions to identify the most effective configurations

As a result, Octank has significantly reduced their development time, improved chatbot response quality, and achieved more consistent performance across product lines with increased reuse of artefacts.

Conclusion

The new Prompt Management and Flows features in Amazon Bedrock represent a significant leap forward in generative AI development. By streamlining workflow creation, prompt management, and team collaboration, these tools enable faster time-to-market and higher-quality AI-powered solutions.

We invite you to explore these new features in preview and experience firsthand how they can improve your generative AI development process. To get started, open the Amazon Bedrock console or discover the new APIs in the Amazon Bedrock SDK, and begin creating your prompts and flows today.

We’re excited to see the innovative applications you’ll build with these new capabilities. As always, we welcome your feedback through AWS re:Post for Amazon Bedrock or your usual AWS contacts. Join the generative AI builder community at community.aws to share your experiences and learn from others.

Stay tuned for more updates as we continue to enhance Amazon Bedrock and empower you to build the next generation of AI-powered applications!

To learn more, refer to the documentation on prompt management and prompt flows for Amazon Bedrock.

About the Authors

Antonio Rodriguez is a Sr. Generative AI Specialist Solutions Architect at AWS. He helps companies of all sizes solve their challenges, embrace innovation, and create new business opportunities with Amazon Bedrock. Apart from work, he loves to spend time with his family and play sports with his friends.

Jared Dean is a Principal AI/ML Solutions Architect at AWS. Jared works with customers across industries to develop machine learning applications that improve efficiency. He is interested in all things AI, technology, and BBQ.