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.