Amazon Bedrock: Building Generative AI Applications on AWS
Amazon Bedrock is a fully managed AWS service for building generative AI applications using foundation models from Amazon and other providers. This article explains how Bedrock works, its foundation models, Knowledge Bases, RAG, Agents, Guardrails, security, and real-world use cases. Learn how developers can build scalable, secure, and intelligent AI applications on AWS without managing model infrastructure.
Generative AI has changed how developers build applications. Instead of developing an artificial intelligence model completely from scratch, developers can use pre-trained foundation models and integrate them into applications.
Amazon Bedrock is a fully managed AWS service that provides access to foundation models from Amazon and other model providers through AWS APIs and tools. It enables developers to build and scale generative AI applications without managing the underlying model infrastructure.
What is Amazon Bedrock?
Amazon Bedrock provides access to foundation models through a unified AWS environment.
Foundation models are large AI models trained on broad datasets and designed to support different types of tasks, including text generation, image generation, conversations, and embeddings.
With Bedrock, developers can select an appropriate model, send prompts to it, evaluate its responses, and integrate the model into their applications.
AWS currently provides access to foundation models from multiple providers through Bedrock, including Amazon and other AI companies. The available models and capabilities can vary by AWS Region.
The major advantage is that developers don't have to build and maintain the underlying AI infrastructure themselves.
Why Use Amazon Bedrock?
Building a generative AI application from scratch can require significant infrastructure, model-development, deployment, and maintenance work.
Amazon Bedrock simplifies this process by providing managed access to foundation models along with tools for building production applications.
Some important capabilities include:
- Access to multiple foundation models
- Model inference through APIs
- Model evaluation
- Retrieval-Augmented Generation (RAG)
- Knowledge Bases
- Agents
- Guardrails
- Model customization
- Integration with other AWS services
This allows developers to focus more on the application and business logic rather than managing AI infrastructure.
Foundation Models in Amazon Bedrock
A foundation model is a general-purpose AI model trained on a large amount of data.
Different models can have different strengths. For example, one model may be suitable for conversational applications, while another may be better suited for coding, summarization, image generation, or other tasks.
Amazon Bedrock provides a model catalog where developers can explore available models and select one based on their application's requirements.
A typical workflow looks like this:
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User Application
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Amazon Bedrock API
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Foundation Model
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Generated Response
Developers can also evaluate models and compare their outputs before selecting a model for a particular use case.
How Amazon Bedrock Works
A simple Bedrock application can follow these steps:
Step 1: Select a foundation model
The developer selects an available foundation model based on the application's requirements.
Step 2: Create a prompt
The application sends instructions or a question to the model.
For example:
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Explain cloud computing to a beginner in simple terms.
Step 3: Send the request
The application sends the request to Amazon Bedrock through an AWS API.
Step 4: Model inference
The selected foundation model processes the input and generates a response.
Step 5: Return the response
The application displays or processes the generated result.
This basic workflow can be extended with Knowledge Bases, Agents, Guardrails, and other Bedrock capabilities.
Amazon Bedrock Knowledge Bases and RAG
One limitation of general-purpose foundation models is that they may not have access to an organization's private or frequently changing information.
This is where Retrieval-Augmented Generation (RAG) becomes useful.
RAG allows an application to retrieve relevant information from external data sources and provide that information to the foundation model before generating a response.
Amazon Bedrock Knowledge Bases provides managed capabilities for implementing RAG workflows. It can retrieve relevant information from connected data sources and use that information to generate more relevant responses.
A simplified architecture looks like this:
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User Question
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Knowledge Base
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Retrieve Relevant Information
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Foundation Model
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Generated Answer
For example, a university could create a chatbot that answers questions about its courses, policies, and academic documents.
Instead of relying only on the model's general knowledge, the application can retrieve relevant information from the university's documents and use it to generate the response.
Amazon Bedrock Agents
Generative AI applications can become more powerful when they can perform actions rather than simply generate text.
Amazon Bedrock Agents can help developers build applications where an AI agent can orchestrate tasks using foundation models, instructions, action groups, and knowledge bases.
For example, imagine an online shopping assistant.
A basic chatbot might answer:
"Your order is currently being processed."
An agent-based application could potentially interact with backend systems to retrieve order information and perform supported actions.
A simplified workflow could be:
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User
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Bedrock Agent
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+----> Knowledge Base
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+----> Action Group
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+----> Foundation Model
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Response
This makes agents useful for applications that require multiple steps or interactions with external systems.
Amazon Bedrock Guardrails
Generative AI applications need appropriate safety and privacy controls.
Amazon Bedrock Guardrails provides configurable safeguards that can evaluate user inputs and model responses.
Guardrails can be configured with policies such as content filters, denied topics, sensitive-information filters, word filters, and other controls. They can also be used with foundation-model inference, Agents, Knowledge Bases, and Flows.
For example:
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User Input
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Guardrail
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+---- Blocked ----> Safe Response
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Foundation Model
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Guardrail
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Final Response
This adds an additional safety layer between users, AI models, and applications.
Amazon Bedrock and Customization
Different applications may require models to behave according to specific requirements.
Amazon Bedrock supports capabilities for customizing models and working with application-specific data. Depending on the model and feature, developers can use techniques such as fine-tuning or continued pre-training, while RAG can provide external context without changing the model itself.
For many applications, RAG can be useful when the goal is to provide a model with information from private or frequently updated data sources.
Common Use Cases
Amazon Bedrock can be used to build many types of generative AI applications.
1. AI Chatbots
Organizations can build conversational applications for customer support, education, internal assistance, and other use cases.
2. Document Summarization
Bedrock can be integrated into applications that summarize large amounts of text or documents.
3. Question-Answering Systems
With Knowledge Bases and RAG, applications can answer questions using information from an organization's data.
4. AI Assistants
Developers can combine foundation models, Agents, and external systems to create assistants capable of completing multi-step tasks.
5. Content Generation
Applications can generate text and other supported content based on user prompts and application requirements.
6. Enterprise Knowledge Search
Organizations can use Knowledge Bases to allow users to search and interact with internal information using natural language.
Amazon Bedrock vs Building Your Own AI Infrastructure
There are different ways to build generative AI applications.
One approach is to manage the model infrastructure yourself. This can require infrastructure provisioning, model deployment, scaling, monitoring, and maintenance.
With Amazon Bedrock, AWS provides managed access to foundation models and additional tools for building generative AI applications.
| Area | Amazon Bedrock | Self-Managed AI Infrastructure |
|---|---|---|
| Infrastructure management | AWS managed | Customer managed |
| Model access | Multiple supported models | Depends on deployment |
| Scaling | Managed service capabilities | Customer responsibility |
| Application integration | AWS APIs and services | Custom integration |
| RAG | Managed Knowledge Bases available | Build/manage pipeline |
| AI safety | Bedrock Guardrails | Implement separately |
| Agents | Bedrock Agents | Build agent architecture |
The appropriate approach depends on the application's requirements, level of control, and operational needs.
Example Architecture
A production-style generative AI application could combine several AWS services:
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Users
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Web Application
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Amazon Bedrock
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+-------------+-------------+
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Foundation Knowledge Guardrails
Model Base
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Private Data
For more advanced applications, Bedrock Agents can also be incorporated to allow the application to interact with supported actions and data sources.
Security Considerations
Security is an important part of building generative AI applications.
Amazon Bedrock integrates with AWS security capabilities such as IAM, allowing organizations to control which identities can access Bedrock resources and operations. AWS also provides security and compliance documentation for configuring Bedrock according to organizational requirements.
Developers should consider:
- IAM permissions
- Data access controls
- Encryption requirements
- Sensitive information
- Application-level authorization
- Guardrails
- Monitoring and logging
- Model and prompt evaluation
Generative AI applications should be designed with security and responsible AI considerations from the beginning.
Getting Started with Amazon Bedrock
A beginner can start by exploring the Amazon Bedrock console and experimenting with available models.
A simple learning path is:
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Foundation Models
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