The generative AI customization spectrum: From prompt engineering to custom models on AWS
AWS maps the customization ladder: when to prompt-engineer vs. fine-tune vs. train from scratch.
Why it matters
Practitioners building with generative AI on AWS need a decision framework to avoid over-engineering. This eight-step guide walks the spectrum from low-cost prompt tweaks to custom model training, helping teams pick the right lever for their use case — and avoid wasting budget on fine-tuning when RAG would do.
The key facts
10 to know8-step decision framework covering prompt engineering, RAG, fine-tuning, continued pre-training
Amazon Nova Forge positioned as the custom model endpoint
Emphasis on 'start simple and escalate only when you must'
AWS Bedrock ecosystem context (implied)
Published Sept 14, 2026
8-step decision framework for AI customization
Approaches covered: prompt engineering, RAG, fine-tuning, continued pre-training, Amazon Nova Forge
Emphasis on starting simple and escalating only when necessary
AWS Bedrock/SageMaker context implied
Published by AWS ML blog (vendor content)
Go to the source
AWS Machine Learning Blogaws.amazon.com
Publisher excerpt: Pick the right generative AI customization approach on AWS with an 8-step decision framework, from prompt engineering and RAG to fine-tuning, continued pre-training, and Amazon Nova Forge. Start simple and escalate only when you must.