A comprehensive guide to building a production-grade AI engineering stack, covering nine core layers: product workflow, prompt management, model routing, context and retrieval, evaluation, observability and tracing, dataset management, deployment and release, and cost/latency controls. Key advice includes defining workflow requirements before choosing a model, keeping prompts versioned and visible, building evals early (even 30–50 examples), logging full LLM request paths, and treating agents as a last resort rather than a default. A phased rollout approach is outlined, from basic prototype tracking through to full release controls with canary deployments and A/B testing.
Table of contents
How to Build an AI Engineering StackStart with the Workflow, Not the ModelThe Core Layers of an AI Engineering StackBe Careful with AgentsA Practical Reference ArchitectureCommon Mistakes When Building an AI Engineering StackRecommended Screenshots and Examples to IncludeHow to Build the Stack in PhasesWhat Good Looks LikeFinal Checklist2 Impressions