Advanced fine-tuning techniques for multi-agent orchestration: Patterns from Amazon at scale

Our work with large enterprise customers and Amazon teams has revealed that high stakes use cases continue to benefit significantly from advanced large language model (LLM) fine-tuning and post-training techniques. In this post, we show you how fine-tuning enabled a 33% reduction in dangerous medication errors (Amazon Pharmacy), engineering 80% human effort reduction (Amazon Global Engineering Services), and content quality assessments improving 77% to 96% accuracy (Amazon A+). These aren’t hypothetical projections—they’re production results from Amazon teams. While many use cases can be effectively addressed through prompt engineering, Retrieval Augmented Generation (RAG) systems, and turn key agent deployment,, our work with Amazon and large enterprise accounts reveals a consistent pattern: One in four high-stakes applications—where patient safety, operational efficiency, or customer trust are on the line—demand advanced fine-tuning and post-training techniques to achieve production-grade performance.

This post details the techniques behind these outcomes: from foundational ...