The generative AI customization spectrum: From prompt engineering to custom models on AWS
TL;DR
AWS lays out an eight-step decision framework for choosing how far to customize generative AI. The first steps use foundation models as they are or improve results with prompt engineering, followed by RAG, prompt caching and distillation, all without touching model weights. Only steps six to eight change the model itself: fine-tuning, continued pre-training on more than a billion tokens and fully custom models built with Amazon Nova Forge.
Nauti's Take
The guide offers teams a real opportunity to avoid expensive training projects, since it makes clear that most use cases get by with prompts and RAG. The catch is that it is vendor guidance leading straight to Bedrock, SageMaker and Nova Forge, and the performance figures come from AWS itself.
Anyone planning an AI project can use the step logic as a checklist, while comparing costs and lock-in with other providers.
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