The AWS Machine Learning Blog outlines an 8-step decision framework to help teams choose an appropriate generative AI customization approach on AWS. The spectrum ranges from prompt engineering and retrieval-augmented generation (RAG) to fine-tuning, continued pre-training, and building custom models with Amazon Nova Forge. The core recommendation is to begin with simpler methods and move to more involved approaches only when necessary.
Why it matters
Selecting the right customization method affects the cost and complexity of deploying generative AI. A structured framework can help teams avoid over-engineering by matching the approach to actual requirements.
Who should care
Organizations and practitioners building generative AI applications on AWS who need to weigh options between lighter-weight techniques and full custom model development.