AWS Generative AI Spectrum: Prompt Engineering to Custom Models
This post outlines an 8-step decision framework for selecting the right approach to customize generative AI models on AWS. The spectrum ranges from using existing models as-is, through prompt engineering and retrieval-augmented generation (RAG), to fine-tuning or training a custom model. Each step increases in effort, cost, and data requirements but also grants more control over domain-specific…
Key points
- Generative AI customization spectrum outlines steps from prompt engineering to fine-tuning
- Each step in the spectrum increases effort, cost, and data requirements but also grants more control over domain-specific tasks
- Fine-tuning or training a custom model is recommended only when simpler methods fail to meet accuracy, latency, or domain requirements
The generative AI customization spectrum: From prompt engineering to custom models on AWS
AWS Machine Learning Blog · 14 September 2026
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