AI engineering is the work of building useful applications on top of ready-made foundation models, rather than training models from scratch.
In plain words
Large pretrained models are now a tool any developer can use. AI engineering is about turning them into products: choosing a model, writing prompts, adding RAG or fine-tuning, and evaluating whether the app actually works. It borrows proven methods from other engineering fields and adds new ones that only make sense with foundation models, such as detecting and reducing hallucinations.
A quick example
Imagine a team building a tool that summarises customer calls. They do not train a model. They pick an existing one, write and test prompts, decide how to measure whether summaries are accurate, check for made-up details, and keep improving the app based on those measurements.
Where you'll meet it
- Building chatbots, assistants and AI features in products.
- Evaluating and improving apps built on LLMs.
Related
Sources
- DocsBooks, Chip Huyen · read 29 Sept 2026
- DocsAI Engineering (book repository), Chip Huyen (GitHub) · read 29 Sept 2026
Checked by WhatIsAI against the sources above · 29 Sept 2026. Spotted an error? Report it.