> For the complete documentation index, see [llms.txt](https://jamiewen00.gitbook.io/ai-engineering-handbook/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://jamiewen00.gitbook.io/ai-engineering-handbook/fundamentals/shift.md).

# ML to AI Shift

The ready-to-use foundation models transforms how engineers build AI. It enables engineers to build advanced apps without needing to understand gradient descent or train models yourself.

## Adapting vs Building

Traditional ML: Train custom models from scratch, requiring deep expertise.

AI Engineering: Use existing foundation models and adapt them through: prompt engineering, context construction (RAG), fine-tuning

## Harder Evaluation

AI Engineering: Open-ended outputs without single "correct" answers. Requires new methods:

* AI-as-judge approaches
* Comparative techniques

## New Focus

* Evaluation of model behavior
* Mastery prompt engineering skills
* Building effective context (e.g. RAG)
* Designing intuitive user interfaces

## Changed Workflow

More AI engineers today are coming from web or full-stack backgrounds, not just traditional ML. AI Engineering now feels much closer to full-stack development:

* Begin with the product and user experience
* Integrate models and data as needed
* Focus shifts from model-first to product-first thinking

## Competitive Advantage

Proprietary data and user feedback become key advantages. As models become common, unique data sets your applications apart.


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