AWS Generative AI Model Agility Solution: A comprehensive guide to migrating LLMs for generative AI production

This article presents a comprehensive framework for migrating and upgrading large language models (LLMs) in production generative AI applications, addressing both technical and operational challenges.

• Three-step migration approach: evaluate source model, optimize prompts for target model, evaluate target model
• Amazon Bedrock Prompt Optimization and Anthropic Metaprompt tool automate prompt migration and optimization
• Evaluation uses predefined metrics (Ragas, DeepEval, Amazon Bedrock Evaluations) and custom LLM-as-judge approaches
• Comprehensive dataset preparation with ground truth answers, latency, and token metrics essential for success
• Model selection considers modalities, context window, cost, performance, quality, specialization, and data privacy
• Evaluation focuses on accuracy/quality, latency (total and time-to-first-token), and cost per inference
• Error analysis and iterative optimization lifecycle improve answer quality and reduce latency
• Migration timeline ranges from two days to two weeks depending on use case complexity
• Monitoring and quality assurance processes reuse established evaluation and ground truth collection methods

This framework enables organizations to systematically migrate LLMs while maintaining performance, reducing costs, and minimizing operational disruption through standardized processes and automated tools.