AI and Software Architecture Learning Hub

Microservices Architecture Basics

Proven patterns for common AI system designs. Each pattern includes components and key considerations.

AI Microservices Architecture

Decompose AI applications into independent services: embedding service, retrieval service, LLM service, and application logic.

RAG Pipeline Architecture

End-to-end pipeline for retrieval-augmented generation: ingestion, indexing, retrieval, and generation.

Agent Architecture

Multi-component system for autonomous AI agents: planning, tool use, execution, and memory.

Streaming Architecture

Real-time response delivery for better UX: token streaming, progressive rendering, and cancellation.

Cost Optimization Architecture

Reduce AI costs through caching, model selection, and request optimization.

Observability Architecture

Monitor, debug, and improve AI systems with comprehensive observability.

AI Workflow Architecture

Every architecture decision involves trade-offs. Understanding these helps you make informed choices.

Latency vs Quality

Smaller models, less retrieval context, aggressive caching

Larger models, more retrieval steps, re-ranking

Model routing, selective re-ranking, tiered retrieval

Cost vs Scale

Smaller models, limited retrieval, fewer features

Largest models, extensive retrieval, all features

Caching, model selection, request batching

Complexity vs Reliability

Fewer components, straightforward flow, easier debugging

Agents, multi-step reasoning, tool use, more failure modes

Start simple, add complexity with guardrails

Ready to Implement?

Architecture patterns come alive in real projects. Join a free workshop to see these patterns in action.

From Idea to Production AI System

Every production AI system follows this lifecycle. Understand each phase to avoid common architectural mistakes.

Define Requirements & Constraints

Identify what the AI system needs to do, latency/cost budgets, data sources, and user expectations. Most failures start here.

Design Architecture

Choose patterns (RAG, agents, pipelines), define service boundaries, select models, and plan data flow between components.

Prototype & Validate

Build minimal working version. Test core assumptions — model quality, retrieval accuracy, latency targets, and cost per request.

Build Production System

Add error handling, caching, monitoring, rate limiting, and scaling. This is where architecture patterns matter most.

Deploy & Monitor

Ship with observability. Track quality metrics, costs, latency, and user feedback. Set up alerts for degradation.

Iterate & Improve

Use feedback loops to improve prompts, retrieval quality, and model selection. Architecture should support continuous improvement.

RAG System Architecture

Visual representation of how components connect in production AI systems.

RAG Pipeline Flow

Agent Execution Flow

Event-Driven AI Architecture

Cost-Optimized Routing

Learn Architecture by Building

Join the free workshop to see these patterns in action. Build a working AI system in 2 hours.