AI Engineering Patterns
14 named patterns across 10 pillars. Trade-offs, implementation guides, and code examples for every layer of a production AI system.
What are you building?
Section titled “What are you building?”Building a RAG system
Retrieve from your own data reliably with structured, searchable pipelines.
Start here Deploying to production
Make AI services reliable, cost-efficient, and observable at scale.
Start here Explore by pillar
Section titled “Explore by pillar” Inference & Serving Gateway routing, semantic caching, model routers, and fallback chains.
Data Patterns Data contracts, feature stores, training pipelines, and eval datasets.
Reliability & Resilience Circuit breakers, graceful degradation, canary deployments, and model rollback.
Retrieval & Memory Hybrid search, RAG, reranking, and contextual memory.
Observability Span-level tracing, cost attribution, and quality drift detection.
Security & Trust Input sanitization, output validation, audit trails, and PII scrubbing.
Cost & Efficiency Token budgets, tiered models, prompt compression, and cost circuit breakers.
Governance & Compliance Model cards, data lineage, policy-as-code, and model versioning.
Graph Patterns GraphRAG, graph-based reasoning, and entity resolution.
Evaluation & Testing LLM-as-Judge, eval pipelines, regression testing, and benchmarking.
Why this exists
Section titled “Why this exists” Not an awesome list Structured, opinionated patterns with named trade-offs and explicit anti-use-cases.
Not vendor docs Framework-agnostic, model-agnostic, vendor-neutral. The patterns, not the products.
Contribute a pattern Open-source and community-maintained. Every pattern goes through structured review.