AWS's 8-Step Framework for Choosing the Right Generative AI Customization Strategy

AWS published an eight-step decision framework for choosing how to customize a generative AI model, ranging from zero-touch prompt engineering to full custom model training. The framework groups decisions into three zones: USE (as-is deployment), ENHANCE (RAG and caching), and TRAIN (fine-tuning and custom pre-training).

The core principle is "start simple, escalate only when you must." The piece walks through real cases from DoorDash, Fractal Analytics, EXL, and Trellix to show where each level of customization paid off.

One standout finding: model distillation produces models that run 500% faster and cost 75% less, with less than 2% accuracy loss compared to the source model. AWS's Nova Forge SDK, available on GitHub since March 2026, supports this workflow.

Key Takeaways

• AWS's 8-step framework organizes AI customization into USE, ENHANCE, and TRAIN zones, with a "start simple" principle throughout
• Model distillation delivers 500% speed gains and 75% cost reduction with under 2% accuracy loss
• Real deployments at DoorDash, Fractal Analytics, EXL, and Trellix illustrate when each customization level is worth the complexity

Read the full article on AWS Machine Learning Blog