The generative AI customization spectrum: From prompt engineering to custom models on AWS
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.
Title: The Generative AI Customization Spectrum: From Prompt Engineering to Custom Models on AWS
AWS offers access to various generative AI foundation models from Anthropic, Meta, Mistral, and Amazon through Amazon Bedrock. Teams can utilize these models for tasks like chatbots, code assistants, document processors, and autonomous agents. However, determining the right customization approach for a specific workload can be challenging due to the numerous options available. Choosing the wrong approach can lead to significant expenses in compute, time, and credibility issues.
The generative AI customization spectrum on AWS provides an 8-step decision framework to help teams pick the right approach without over-engineering or under-investing. The spectrum starts with using existing models as-is and progresses to training custom models from scratch. This approach balances simplicity, cost, data requirements, and control while enabling teams to escalate to more complex methods only when necessary.
The spectrum comprises three categories:
1. USE: Utilize existing models without modification or improve prompts with system instructions, few-shot examples, and chain-of-thought reasoning. This step offers a quick and cost-effective solution for most general-purpose tasks.
2. ENHANCE: Add external tools, data sources, and optimizations while leaving the model's weights untouched. This step includes Grounded Language Modeling (RAG), prompt caching, and distillation of large models into smaller, faster ones.
3. TRAIN: Update model weights with labeled data (fine-tuning), expand foundational knowledge with massive unlabeled corpora (continued pre-training), or build a fully custom model from scratch (Amazon Nova Forge).
To determine the appropriate step, teams should follow an escalation flow, moving up only when the current step fails to meet accuracy, latency, or domain requirements. For most workloads, the customization spectrum rarely extends beyond Step 3.
By following the generative AI customization spectrum on AWS, teams can optimize their generative AI solutions, ensuring they invest only the necessary level of effort, cost, and data while achieving the desired results.
Written by urgent.news from AWS Machine Learning's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
