Building Generative AI Workflows with Amazon Bedrock
Generative AI transforms how applications interact with users by enabling dynamic, context-aware, and highly personalized responses. Amazon Bedrock provides a powerful platform for building and managing foundational models, prompt flows, and AI-driven agents. This Cloud Lab uses Amazon Bedrock to design intelligent workflows that support user interactions within a cloud learning platform.
In this Cloud Lab, you will explore Amazon Bedrock’s foundational components, including prompt management, Bedrock Agents, Knowledge Bases, and pretrained foundational models. You’ll understand how Bedrock integrates with other AWS services to provide a seamless environment for managing AI capabilities. Moreover, you’ll set up a Lambda function to analyze the query’s category and an RDS database to store and manage embeddings, enabling efficient retrieval and contextual responses.
Moving forward, you’ll design and deploy Prompt Flows tailored to various user queries. These flows will ensure that responses align with user intents and context. You’ll also implement strategies for dynamically adapting prompts based on the user’s query complexity.
Finally, you’ll simulate real-world scenarios by building a support system for Cloud Labs. This system will leverage Bedrock agents to respond intelligently to user queries, ranging from basic information requests to assisting them with setting up their infrastructure for different scenarios.
After completing this Cloud Lab, you will gain hands-on experience creating AI-driven workflows that enhance user engagement. You’ll also better understand how foundational models, Prompt Flows, and data storage solutions work together to deliver robust and scalable AI applications.
Why Amazon Bedrock workflows matter in real applications
Generative AI gets interesting when it stops being a single prompt-and-response demo and starts behaving like part of a product. Real applications need to route requests, retrieve context, apply guardrails, and coordinate multiple steps before returning an answer a user can trust.
That’s the core value of building workflows with Amazon Bedrock: you can combine foundation models with structured orchestration so your app responds consistently, even as user requests vary from “quick question” to “multi-step support issue.”
What “workflow” means in Bedrock Flows
In practice, a workflow is a sequence of connected steps, often including:
A prompt (or prompt template)
A model invocation
Optional retrieval for grounding (for example, via knowledge bases)
Business logic (like classification or routing)
A final response that matches the user’s intent
Bedrock Flows is designed to help you build, test, and deploy these workflows using a visual builder, linking prompts, models, and integrations (like Lambda) into an end-to-end system.
Common building blocks you’ll use again and again
When you build AI workflows that scale beyond a prototype, you’ll repeatedly rely on a few patterns:
Prompt management and reuse: As soon as you have multiple intents (billing questions, troubleshooting, onboarding, “how-to” help), you’ll want prompts that are versioned, consistent, and easy to update without breaking everything downstream.
Agents for tool use and multi-step tasks: Agents become useful when the model needs to do more than “generate text,” for example, deciding what to do next, calling a tool, or following a structured plan to complete a task.
Knowledge and retrieval for grounded answers: Many support and internal-assistant experiences aren’t about creativity; they’re about correctness. Grounding a model in relevant documents (RAG-style) reduces hallucinations and makes responses more context-aware, especially for product- or organization-specific information.
Routing and classification: A simple but high-impact step is to classify a query first (what is the user asking?) before sending it to the right prompt flow or agent. That’s why pairing a lightweight classifier (often via AWS Lambda) with your Bedrock workflow is a practical, repeatable design.
Vector storage for semantic retrieval: When you’re storing embeddings for retrieval, you need a place to keep them and query them efficiently. This lab specifically calls out Aurora Serverless as a vector embedding store, which is a common “production-ish” choice when you want relational reliability plus modern retrieval support.
How to know you built something “real,” not just a demo
If your workflow can:
Handle different categories of user requests.
Pull in relevant context when needed.
Adapt its prompt strategy based on complexity.
Use agents to perform multi-step reasoning or actions.
Produce consistent responses across repeated runs.
…you’re much closer to an application pattern you can reuse at work. This Cloud Lab’s “support assistant” scenario is a solid model for that kind of end-to-end build.