This content originally appeared on DEV Community and was authored by Nimmala NAGA SANTHOSH BABA
Stop Reinventing the Wheel 🛑
When you start building with AI on AWS, you'll realize something quickly: Everyone is trying to solve the same problems.
- "How do I chat with my PDF documents?" (RAG)
- "How do I run this cheaply without managing servers?" (Serverless)
- "How do I make the AI take action, not just talk?" (Agents)
The good news? AWS has already published production-ready blueprints for these.
In this post, I’ve curated the best architectural patterns and Proof-of-Concept (POC) repositories directly from the AWS team. Treat this as your "Cheat Sheet" for starting any AI project.
Pattern 1: The "Chat with Your Data" (RAG) 📚
The Problem: LLMs (like Claude or GPT) don't know about your private data. They hallucinate when asked about your specific company policies.
The Solution: Retrieval Augmented Generation (RAG). You "retrieve" the right page from your documents first, then send it to the AI to "generate" an answer.
🏗️ The Architecture
- Ingestion: Upload PDFs to Amazon S3.
- Embedding: Amazon Titan converts text into numbers (vectors).
- Storage: Amazon OpenSearch Serverless stores these vectors.
- Retrieval: Amazon Bedrock Knowledge Bases finds the right chunk and answers the question.
🚀 The "One-Click" POC
Don't build this from scratch. AWS provides a fully managed solution where you just point to your S3 bucket.
- 🔗 Official Repository: Amazon Bedrock RAG Workshop
- 📖 Must-Read Blog: Build RAG applications with Amazon Bedrock Knowledge Bases
Pattern 2: The "Penny Pincher" (Serverless AI) 💸
The Problem: Hosting your own AI models on GPU servers (EC2) costs a fortune ($1000s/mo), even when no one is using them.
The Solution: Serverless Generative AI. Use AWS Lambda to call Amazon Bedrock. You pay only for the milliseconds the AI is thinking. Zero idle cost.
🏗️ The Architecture
- Frontend: React/Next.js hosted on AWS Amplify.
- API: Amazon API Gateway receives the user's prompt.
- Brain: AWS Lambda (Python/Node.js) receives the request and calls Bedrock.
- Model: Bedrock generates the text/image and returns it.
🚀 The "One-Click" POC
This is the most cost-effective way to launch an MVP.
- 🔗 Official Repository: Serverless Generative AI SaaS Boilerplate
- 📖 Must-Read Blog: Build a Serverless Generative AI App with AWS Lambda and Bedrock
Pattern 3: The "Do-er" (AI Agents) 🕵️♂️
The Problem: Chatbots are passive. They can talk, but they can't do anything (like book a flight, query a database, or send an email).
The Solution: Amazon Bedrock Agents. You give the AI a set of "Tools" (Lambda functions), and it figures out which one to use to solve the user's problem.
🏗️ The Architecture
- User Request: "Book a meeting with John for next Tuesday."
- Agent Router: Bedrock Agent analyzes the request.
- Action: It decides to call the
CheckCalendartool first, then theSendInvitetool. - Execution: It triggers the respective Lambda functions to actually perform the tasks.
🚀 The "One-Click" POC
- 🔗 Official Repository: Build an Insurance Agent with Amazon Bedrock
- 📖 Must-Read Blog: Automate tasks with Agents for Amazon Bedrock
Pattern 4: The "Event-Driven" Analyst ⚡
The Problem: You want AI to run automatically in the background—for example, every time a new file is uploaded, summarize it.
The Solution: EventBridge Pipes. Connect your S3 bucket directly to your AI workflow.
🏗️ The Architecture
- Trigger: User uploads a document to S3.
- Event: EventBridge detects the "Object Created" event.
- Process: It triggers a Step Function workflow.
- AI Task: The workflow calls Bedrock to summarize the document and saves the summary to a database.
🚀 The "One-Click" POC
- 🔗 Official Repository: Serverless Document Summarization
Summary Checklist ✅
| Use Case | Recommended Pattern | Key Services |
|---|---|---|
| Q&A on Documents | RAG | Bedrock Knowledge Bases, OpenSearch |
| Low-Cost MVP | Serverless | Lambda, API Gateway, Bedrock |
| Complex Tasks | Agents | Bedrock Agents, Lambda |
| Background Jobs | Event-Driven | EventBridge, Step Functions |
Save this post. You will need these links when you start your build!
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This content originally appeared on DEV Community and was authored by Nimmala NAGA SANTHOSH BABA
Nimmala NAGA SANTHOSH BABA | Sciencx (2025-11-23T04:10:34+00:00) The AWS AI Architect’s Cheat Sheet: Patterns, POCs, and Blueprints 🏗️. Retrieved from https://www.scien.cx/2025/11/23/the-aws-ai-architects-cheat-sheet-patterns-pocs-and-blueprints-%f0%9f%8f%97%ef%b8%8f/
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