AI Engineering Architecture Map

Map the Whole AI System

Production AI engineering is not just prompting a model. A complete architecture includes data ingestion, retrieval, prompts, schemas, tool calls, orchestration, evaluation, monitoring, security, and deployment controls.

Core Layers

• Interface: chat, API, internal app, workflow trigger, or embedded assistant.
• Context layer: user input, system instructions, memory, retrieved documents, and runtime state.
• Model layer: generation, classification, extraction, planning, or tool-selection behavior.
• Tool layer: APIs, databases, search, file systems, queues, and business actions.
• Control layer: policies, guards, evals, logs, permissions, and human review.

Architect for Change

Models, pricing, latency, and best practices change quickly. Use modular boundaries so you can replace models, tune prompts, swap retrievers, add evals, and restrict tools without rewriting the whole application.