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metodologia-aws-architecture-design

Design AWS GenAI architectures for scalable, secure AI workloads.

Updated Mar 31, 2026
Search Tags:#architecture#aws#bedrock#well-architected#vector-search#genai#sageMaker
One-click install
npx skills add https://github.com/JaviMontano/metodologia-propuesta-agent-public --skill metodologia-aws-architecture-design
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: metodologia-aws-architecture-design
Source: https://github.com/JaviMontano/metodologia-propuesta-agent-public/tree/main/.claude/skills/cloud/aws-architecture-design
Command: npx skills add https://github.com/JaviMontano/metodologia-propuesta-agent-public --skill metodologia-aws-architecture-design

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill helps architects design AWS GenAI architectures that meet the GenAI Lens pillars while balancing security, reliability, and cost efficiency.

Core Features & Use Cases

  • Pattern catalog & service mapping: Provides recommended service choices (Bedrock, SageMaker, OpenSearch Serverless, DynamoDB, S3) and alternative options with rationale.
  • GenAI pattern recipes: RAG on AWS, Agentic Workflows, Fine-Tuning, Multi-Model Routing, Streaming, Batch Processing guidance and decision trees.
  • Migration & optimization guidance: Supports cross-region deployments, multi-account setups, governance considerations, and cost-aware design decisions.

Quick Start

Provide a complete AWS GenAI architecture outline for an AI workload using Bedrock, SageMaker, OpenSearch, and S3.

Frequently Asked Questions about metodologia-aws-architecture-design

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I design a scalable AWS GenAI architecture using Bedrock and SageMaker?▼

To design a scalable AWS GenAI architecture, use Bedrock and SageMaker to manage workloads, implement multi-AZ deployment, and apply Well-Architected GenAI Lens pillars for security, reliability, and cost efficiency.

What is the best way to implement Retrieval-Augmented Generation on AWS?▼

Implementing RAG on AWS involves combining Bedrock for foundation models with OpenSearch Serverless for vector-search and S3 for storage, ensuring private connectivity via VPC Endpoints to maintain data security.

When do I need to use VPC Endpoints and guardrails in my AWS GenAI workload?▼

You need VPC Endpoints and guardrails in your AWS GenAI workload to enforce private connectivity, secure data access, and apply Well-Architected compliance metrics across multi-AZ deployments and agentic workflows.

Can I use this approach for fine-tuning models and managing cross-region deployments?▼

Yes, this approach supports fine-tuning models and managing cross-region deployments by providing decision trees for multi-model routing, batch processing, and scalable cost-management across multi-account setups.

How does multi-model routing work with Amazon Bedrock for streaming and batch processing?▼

Multi-model routing with Amazon Bedrock directs traffic across models for streaming and batch processing, optimizing performance and scalable cost-management while adhering to Well-Architected GenAI Lens pillars.

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