InjazAI

AWS published an eight-step decision framework for choosing how to customize generative AI, escalating from prompt engineering through RAG, fine-tuning and continued pre-training up to Amazon Nova Forge, urging teams to start simple and escalate only when necessary.

1 min read

AWS Generative AI Customization Spectrum: From Prompt Engineering to Custom Models

What AWS published

FAQ

What is the difference between prompt engineering, RAG and fine-tuning?

Prompt engineering rewrites instructions without touching the model, RAG adds external knowledge at inference time, and fine-tuning adjusts model weights to change behavior and style.

When is fine-tuning the wrong choice?

When the problem is missing or fast-changing knowledge. RAG is cheaper, faster to update and better suited than retraining the model.

What is Amazon Nova Forge and who should use it?

It is an advanced path for building fully custom models, suited to organizations with large proprietary datasets and a defensible competitive edge.

Should MENA teams adopt custom models now?

Not as a first step. Prove value with prompt engineering and RAG, then escalate only when a measured gap cannot be closed at the lower level.

Source: AWS Machine Learning

AI-assisted content, human-reviewed.