# AI Engineering AI engineering is the discipline of building applications on top of readily available foundation models ([[Large Language Models (LLMs)]], diffusion models, etc.) rather than training models from scratch. It emerged as a distinct field when the model-as-a-service approach lowered the barrier: you no longer need a PhD in machine learning to build AI-powered software. AI engineering differs from traditional [[Machine Learning (ML)]] engineering in focus. ML engineering is about building and training models. AI engineering is about using existing models effectively: [[Prompt Engineering]], [[Retrieval-Augmented Generation (RAG)]], [[Context Engineering]], fine-tuning, and agent design. The skill set shifted from mathematics and data science to software engineering, systems design, and [[Context Engineering]]. The AI engineering stack typically includes: - **Model selection and [[Model routing]]**: choosing the right model for each task - **[[Context Engineering]]**: assembling the right information for the model - **[[AI Retrieval Patterns]]**: RAG, [[Semantic chunking]], [[Knowledge Graph (KG)|knowledge graphs]] - **Agent frameworks**: [[AI Agent Harness|harnesses]], [[Agentic loops]], [[AI Agent Orchestration]] - **Evaluation**: measuring output quality, detecting [[AI Hallucination|hallucinations]], tracking costs - **Deployment**: latency optimization, [[Token Budget]] management, [[AI Gateway|gateways]] - **Safety**: [[AI Guardrails]], [[AI Alignment]], [[Responsible AI]] [[Agentic Engineering]] is a subset of AI engineering focused specifically on building with coding agents. [[Vibe Coding]] and [[Vibe Engineering]] describe the spectrum of discipline applied when using these tools. Chip Huyen's [[AI Engineering (book)|AI Engineering]] (O'Reilly, 2024) is the definitive reference, covering the full stack from model selection through deployment. ## References - Huyen, C. (2024). *AI Engineering*. O'Reilly Media ## Related - [[Agentic Engineering]] - [[Context Engineering]] - [[Large Language Models (LLMs)]] - [[Machine Learning (ML)]] - [[AI Agents]] - [[AI Agent Harness]] - [[Prompt Engineering]] - [[Retrieval-Augmented Generation (RAG)]] - [[AI Retrieval Patterns]] - [[Model routing]] - [[AI Guardrails]] - [[AI Observability]] - [[Vibe Coding]] - [[Vibe Engineering]] - [[AI Engineering (book)]]