Architecture
Enterprise AI architecture patterns
System design patterns, infrastructure blueprints, and scalable platform architecture for production AI systems.
Design Patterns
Core architecture patterns
Microservices for AI
Decoupled model serving, container orchestration, API gateways, and independent scaling.
Event-Driven AI Pipelines
Real-time data processing with Kafka/Pulsar, stream processing, event sourcing, and CQRS.
RAG Architecture
Retrieval-augmented generation with vector stores, embedding pipelines, and hybrid search.
Model Serving Patterns
Real-time inference, batch processing, edge deployment, and multi-model serving.
Data Platform Architecture
Feature stores, data lakes, lakehouse patterns, and real-time feature serving.
Agentic System Design
Multi-agent orchestration, tool use patterns, memory systems, and planning architectures.
Featured Articles
Architecture deep dives
Designing a Production RAG Architecture
End-to-end architecture for retrieval-augmented generation systems that handle enterprise-scale document collections.
The LLM Gateway Pattern: Centralizing Model Access
How to build a centralized gateway for managing LLM access, cost tracking, rate limiting, and fallback routing.
Advisory AI: Get Brutally Honest Feedback on Your Ideas
5 AI agents inspired by legendary thinkers provide structured critique on your business ideas. Runs locally with Ollama - no API keys required.
Building a Custom Subscription System Without External Tools
How to build your own email subscription system using git hooks, SQLite, and zero external dependencies for complete independence.
Microservices Architecture for AI Systems
Designing microservice architectures that support AI workloads — model serving, feature stores, and orchestration.
Event-Driven AI Pipelines with Kafka
Building real-time AI pipelines using event-driven architecture — from data ingestion to model inference.
Feature Store Architecture for ML Systems
Designing feature stores that serve both batch training and real-time inference workloads.
Multi-Agent System Architecture Patterns
Designing systems where multiple AI agents collaborate — orchestration, communication, and state management.
LLM Caching Strategies for Production Systems
Semantic caching, exact-match caching, and hybrid approaches to reduce LLM costs and latency.
Designing AI-Powered APIs
API design patterns for AI services — streaming responses, async processing, and graceful degradation.
Vector Search Architecture at Scale
Designing vector search systems that handle billions of vectors with low latency and high recall.
ML Pipeline Architecture: From Training to Serving
End-to-end ML pipeline design — data processing, training, evaluation, deployment, and monitoring.
Data Lakehouse Architecture for AI Workloads
Designing data lakehouses that support both analytics and AI training workloads efficiently.
Prompt Chain Architecture Patterns
Designing reliable prompt chains — sequential, parallel, conditional, and recursive patterns.
Security Architecture for AI Systems
Designing secure AI systems — from prompt injection defense to model access control and data protection.
Real-Time ML Serving Architecture
Designing low-latency model serving systems — load balancing, batching, model routing, and auto-scaling.
Observability Architecture for AI Applications
Designing observability systems for LLM applications — tracing, metrics, logging, and alerting.
Hybrid Search Architecture: Combining Vector and Keyword Search
Designing search systems that combine semantic and lexical retrieval for optimal results.
Advanced LLM Gateway Patterns: Multi-Region and Multi-Cloud
Scaling LLM gateways across regions and cloud providers for resilience and compliance.
Document Processing Pipeline Architecture
Designing scalable document ingestion pipelines — parsing, chunking, enrichment, and indexing.
Feedback Loop Architecture for AI Systems
Designing systems that learn from user feedback — RLHF pipelines, implicit signals, and continuous improvement.
Multi-Tenant AI Platform Architecture
Designing AI platforms that serve multiple teams and use cases with isolation, quotas, and governance.
Streaming AI Response Architecture
Designing systems for streaming LLM responses — SSE, WebSockets, backpressure, and client-side rendering.
Batch Processing Architecture for AI Workloads
Designing efficient batch inference systems — job scheduling, resource management, and cost optimization.
Enterprise Knowledge Base Architecture
Designing knowledge management systems powered by AI — ingestion, organization, retrieval, and maintenance.
Testing Architecture for AI Applications
Designing comprehensive test strategies for AI systems — unit, integration, evaluation, and chaos testing.
Embedding Pipeline Architecture at Scale
Designing embedding pipelines that handle millions of documents — batching, versioning, and incremental updates.
Cost Management Architecture for AI Systems
Designing systems for tracking, allocating, and optimizing AI infrastructure and API costs.
Conversational AI Architecture Patterns
Designing chatbot and conversational AI systems — dialog management, context handling, and memory.
AI Workflow Engine Architecture
Designing workflow engines for complex AI tasks — DAG execution, error handling, and human-in-the-loop.
Model Registry Architecture for Enterprise ML
Designing model registries that track lineage, versions, metadata, and deployment status.
Edge AI Architecture: On-Device Inference Patterns
Designing AI systems for edge deployment — model optimization, sync strategies, and offline operation.
A/B Testing Architecture for AI Features
Designing experimentation systems for AI features — traffic splitting, metric collection, and statistical analysis.
Guardrails Architecture for LLM Applications
Designing input/output guardrail systems — content filtering, PII detection, and policy enforcement.
Data Governance Architecture for AI
Designing data governance systems that support AI compliance — lineage, access control, and audit trails.
RAG Evaluation Architecture: Continuous Quality Monitoring
Designing automated evaluation systems for RAG pipelines — retrieval quality, generation quality, and alerting.
AI-Powered Notification Architecture
Designing intelligent notification systems that use AI for relevance scoring, timing, and personalization.
Multi-Model Orchestration Architecture
Designing systems that coordinate multiple AI models — routing, fallback, ensemble, and chain-of-models.
AI Content Generation Pipeline Architecture
Designing content generation systems — from ideation to generation to review to publication.
Semantic Cache Architecture for AI Applications
Designing caching systems that understand semantic similarity — embedding-based cache keys and invalidation.
AI System Migration Architecture
Designing migration strategies for AI systems — model swaps, provider changes, and architecture evolution.
Internal AI Playground Architecture
Designing internal tools for teams to experiment with AI models — sandboxing, cost controls, and sharing.
Compliance Architecture for Regulated AI
Designing AI systems that meet regulatory requirements — audit trails, explainability, and data residency.
Disaster Recovery Architecture for AI Systems
Designing resilient AI systems — failover strategies, data backup, model versioning, and recovery procedures.
Recommendation System Architecture
Designing modern recommendation systems — collaborative filtering, content-based, and LLM-enhanced approaches.
Search Ranking Architecture with AI
Designing AI-powered search ranking systems — learning to rank, neural re-ranking, and personalization.
Data Versioning Architecture for ML
Designing data versioning systems for reproducible ML — DVC, lakeFS, and custom solutions.
Canary Deployment Architecture for AI Models
Designing safe model deployment systems — canary releases, shadow mode, and gradual rollouts.
Tool Use Architecture for LLM Agents
Designing systems that enable LLMs to use external tools — function calling, sandboxing, and error handling.
Memory Architecture for AI Agents
Designing memory systems for AI agents — short-term, long-term, episodic, and semantic memory patterns.
Rate Limiting Architecture for AI APIs
Designing rate limiting systems for AI services — token-based limits, fair queuing, and priority tiers.
Logging Architecture for LLM Applications
Designing comprehensive logging for AI systems — prompt/response logging, PII handling, and analysis.
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