AI Engineering

AI engineering is the discipline of building, integrating, and operating applications and systems on top of models, primarily through API-based integration rather than model training.

Details

AI engineering takes pre-trained models as a given and focuses on the application layer: prompt design, context engineering, tool orchestration, AI workflows, RAG pipelines, agent loops, guardrails, evals, and production concerns like latency, observability, and cost management. The core output is a working AI-powered product or system, not a trained model.

This distinguishes it from ML engineering, which centers on the model lifecycle - data curation, model architecture design, training, fine-tuning, and inference optimization. ML engineering produces models; AI engineering produces systems that use them. The two disciplines overlap at boundaries like fine-tuning and model quantization, but their day-to-day concerns, toolchains, and deliverables differ substantially.