The generative AI customization spectrum: From prompt engineering to custom models on AWS

This article presents an 8-step decision framework for choosing the right generative AI customization approach on AWS, from using models as-is to training custom foundation models.

• Step 1-2 (USE): Call models directly or optimize prompts with system instructions and few-shot examples
• Step 3-5 (ENHANCE): Ground models with RAG, cache prompts, or distill large models into smaller ones
• Step 6-8 (TRAIN): Fine-tune model weights, continue pre-training on domain data, or build custom models with Amazon Nova Forge
• Start at Step 1 and escalate only when current step fails accuracy, latency, or domain requirements
• Most workloads never progress past Step 3 (RAG)
• Common mistakes include jumping to fine-tuning when prompt engineering suffices, or choosing continued pre-training based on data volume alone
• Amazon Bedrock handles Steps 1-7; Amazon SageMaker and Nova Forge support deeper customization

The spectrum prioritizes simplicity and cost-effectiveness by matching customization effort to actual business requirements rather than defaulting to advanced techniques.