The generative AI customization spectrum: From prompt engineering to custom models on AWS
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This AWS blog post presents an 8-step decision framework called the generative AI customization spectrum, intended to help teams choose an appropriate customization level for generative AI workloads without over-engineering or under-investing. AWS provides access to foundation models from Anthropic, Meta, Mistral, and Amazon through Amazon Bedrock, but the post says the harder question is how to adapt them to a specific problem. The spectrum is organized into three categories: USE, which covers using models as-is or improving prompts with system instructions, few-shot examples, and chain-of-thought; ENHANCE, which includes grounding models with RAG, caching prompts, or distilling larger models into smaller ones; and TRAIN, which involves fine-tuning, continued pre-training, or building custom models with Amazon Nova Forge. It advises starting at Step 1 and moving up only when the current approach fails accuracy, latency, or domain needs, noting most workloads never go past Step 3. The article maps each step to AWS services such as Amazon Bedrock, Amazon SageMaker, and Amazon Nova Forge, and uses a chef's kitchen analogy to illustrate tradeoffs. It also lists real-world examples, including Dovetail, DoorDash, Fractal Analytics, EXL, inGenious.ai, Goodnotes, and Trellix, to show outcomes such as faster development, lower costs, and improved call handling. For advanced steps, it describes prompt caching, model distillation, fine-tuning with PEFT/LoRA or full fine-tuning, and reinforcement fine-tuning.
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