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System Design & Scaling Guide
Master architectural thinking for AI systems: scaling strategies, reliability patterns, integration patterns (webhooks, event-driven, MCP), real-world channels (Slack/Discord), and performance vs cost trade-offs. This synthesis guide connects everything you've learned—Docker, CI/CD, deployment, monitoring, cost optimization, security—and teaches you to DESIGN production-ready systems. Culminates with the complete architecture design for the Capstone: AI-Powered Knowledge Assistant with Agentic RAG and Slack/Discord integration.
- 64
- lessons
- 8
- modules
- English · Spanish
- available in
- Yes
- certificate
- Included in the Club
- access
Outcomes
What you'll be able to do
- Apply architectural thinking specific to AI systems (constraints, trade-offs, design documents)
- Compare and choose between monolith, microservices, and event-driven architectures for AI
- Design scaling strategies: horizontal vs vertical, auto-scaling, stateless design
- Implement integration patterns: webhooks, event-driven, MCP (Model Context Protocol)
- Design reliability at scale: load balancing, queue-based processing, rate limiting
- Design Slack and Discord bots as production channels (OAuth, webhooks, best practices)
- Apply decision frameworks for performance vs cost, managed vs self-hosted, cache vs compute
- Design the complete architecture for the Capstone: AI-Powered Knowledge Assistant
Before you start
What you need to bring
It's for you if...
- AI Engineers who completed guides #1-#20 and need to learn system design thinking
- Developers preparing for the Capstone who want the architecture blueprint first
- Tech leads and architects designing AI systems for their teams
- Engineers who want to make informed decisions about scaling, integrations, and trade-offs
- Developers seeking portfolio-worthy architecture design documents
Requirements and materials
- Completion of AI Engineering Path guides #1-#20 (Docker, CI/CD, Deployment, Monitoring, Cost Optimization, Security)
- Experience deploying and monitoring AI applications
- Understanding of RAG, agents, and production patterns
- Familiarity with REST APIs, webhooks, and async patterns
- Basic experience with architecture diagrams (or willingness to learn)
Content
The syllabus, module by module
Open any of them to see its lessons.
- Introduction to System Design Thinking for AI
- Three Constraints Unique to AI Systems
- Structured Trade-off Analysis
- C4 Diagrams: Context and Container
- C4 Diagrams: Component
- Architecture Decision Records (ADRs)
- Reversible vs Irreversible Decisions
- Module 1 Project: Architecture Decision Document
- Introduction to Common AI Architectures
- AI Monolith: When and Why
- AI Microservices: When and Why
- Event-driven Architecture for AI
- Hybrid Architectures: the Real Pattern
- AI-Specific Considerations
- Decision Framework for Choosing an Architecture
- Module 2 Project: Architecture Comparison Document
- Introduction to Scaling Fundamentals
- Horizontal vs Vertical Scaling for AI
- Bottlenecks in AI Systems
- Stateless Design for AI
- AI-Appropriate Auto-scaling Triggers
- Capacity Planning with Numbers
- Cost as a Scaling Constraint
- Module 3 Project: Scaling Analysis Document
- Introduction to Integration Patterns
- Webhooks Inbound
- Webhooks Outbound
- Event-driven with Queues and Pub/Sub
- MCP: Introduction and Concepts
- MCP: Architecture and Implementation
- Decision Framework: When to Use Each Pattern
- Module 4 Project: Integration Architecture Document
- Introduction to Reliability at Scale
- Load balancing for AI services
- Queue-based processing for LLM calls
- Technical and budget rate limiting
- Circuit breakers for LLM providers
- Retry strategies and idempotency
- Graceful degradation by levels
- Project: Reliability Design Document
- Introduction to Real-World Integrations — Slack/Discord
- Slack Events API and Web API
- Discord Gateway and slash commands
- OAuth 2.0 for bots — multi-tenant
- Rate limits and timeouts per platform
- Rich message formatting
- Decision framework: Slack vs Discord vs both
- Project: Slack/Discord Bot Design Document
- Introduction to Performance vs Cost Trade-offs
- Scale Up vs Scale Out for AI
- Cache vs Compute
- Managed vs Self-hosted
- Serverless vs Containers
- Model routing and multi-model
- Decision Matrix template
- Project: Trade-off Decision Matrix
- Introduction to Capstone Architecture Design
- System Overview and C4 diagrams
- RAG Pipeline Design
- Agent Layer Design
- Integration Layer
- Scaling + Reliability applied to the Capstone
- Stack decisions + cost projection
- Final project: Capstone Architecture Design Document
Where it fits
This guide is part of something bigger
It's studied inside these programs, with support and dates.
Common questions
What people usually ask
As long as your Club subscription is active. If you cancel and come back later, you get the access and your progress back.
No. Modules run from easier to harder, but you can jump to the one you need. Progress is saved per lesson.
Whatever is needed is listed under “What you need to bring”, above. If nothing is listed there, you can start from zero.
In the Club's WhatsApp group, and every two weeks there's a live with an instructor where questions get worked through.
Yes. It's issued automatically once you finish every lesson, with a verifiable code you can share on LinkedIn.
No. This guide is self-paced with no dates. The bootcamp is live, by cohort, with work someone reviews.
Start whenever you like
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