ArchitectDistributed Systems.

Design resilient microservices using AI. From service discovery to event sourcing, learn the senior frameworks that prevent architectural decay.

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Why AI-generated architecture usually fails

Most AI prompts result in 'Lego architecture'—pieces that look right but won't fit together in production.

The "Junior" Approach

✗ Asking AI for a "microservices diagram" without context
✗ Accepting generic service boundaries
✗ Ignoring data consistency across service calls
✗ Building a distributed monolith by accident

The Senior AI Workflow

✓ Decomposing by bounded contexts first
✓ Validating async message schemas with AI
✓ Prompting for failure modes and resilience
✓ Using AI to generate Kubernetes/IaC boilerplate

Mastering Distributed Workflows

From high-level orchestration to granular service implementation.

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Bounded Contexts

Learn how to guide AI to find the right service boundaries using Domain-Driven Design principles.

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Event Schemas

Automate the generation of ProtoBuf or Avro schemas for reliable cross-service communication.

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Resilience Patterns

Systematically prompt for circuit breakers, retry logic, and fallback strategies that actually work.

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Observability

Use AI to instrument your services with OpenTelemetry and meaningful logging without the manual slog.

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Infrastructure as Code

Turn architectural decisions into Terraform or CloudFormation templates in minutes.

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Contract Testing

Write robust consumer-driven contract tests using AI to ensure services never break each other.

Traditional vs AI-Assisted Design

Task	Traditional Method	With AI Mastery
API Design	2-3 days of manual spec writing	1 hour (Schema-first generation)
Service Discovery	Manual config & plumbing	Automated K8s/Consul orchestration
Error Handling	Hand-written try/catch loops	Consistent resilience middleware
Documentation	Outdated README files	Live-synced AI knowledge base

FAQ

Can AI really help with high-level system design?

Yes, but not by "drawing" the diagram for you. It helps by validating assumptions, generating boilerplate for service-to-service communication, and suggesting resilience patterns (circuit breakers, retries) based on specific constraints you provide.

Does the course cover specific cloud providers?

We focus on tool-agnostic patterns (Kubernetes, Kafka, gRPC, REST) and how to prompt AI to generate configurations for AWS, GCP, or Azure based on those patterns.

How does this differ from the general architecture chapter?

This page highlights our specific focus on distributed systems—tackling issues like data consistency, service discovery, and message queue integration which require much tighter context control than monolithic design.

Stop building distributed monoliths.

The mental models for distributed systems are the most valuable skill a senior dev can have. Learn how to leverage AI to ship them 10x faster.

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