Enterprises are increasingly shifting from relying solely on large, general-purpose language models to developing specialized large language models (LLMs) fine-tuned on their own proprietary data. Although foundation models (FMs) offer impressive general capabilities, they often fall short when applied to the complexities of enterprise...
My first attempt at building a travel planning agent looked exactly like most early prototypes: one big model, a few tools, and a long system prompt. It worked fine until the moment real-world complexity showed up. Flights came from a clean API, hotels lived...
Today, we’re announcing structured outputs on Amazon Bedrock—a capability that fundamentally transforms how you can obtain validated JSON responses from foundation models through constrained decoding for schema compliance.
This represents a paradigm shift in AI application development. Instead of validating JSON responses and writing fallback...
Training and deploying large AI models requires advanced distributed computing capabilities, but managing these distributed systems shouldn’t be complex for data scientists and machine learning (ML) practitioners. The command line interface (CLI) and software development kit (SDK) for Amazon SageMaker HyperPod with Amazon Elastic Kubernetes...
In the post Evaluating generative AI models with Amazon Nova LLM-as-a-Judge on Amazon SageMaker AI, we introduced the Amazon Nova LLM-as-a-judge capability, which is a specialized evaluation model available through Amazon SageMaker AI that you can use to systematically measure the relative performance of...
Embedding models power many modern applications—from semantic search and Retrieval-Augmented Generation (RAG) to recommendation systems and content understanding. However, selecting an embedding model requires careful consideration—after you’ve ingested your data, migrating to a different model means re-embedding your entire corpus, rebuilding vector indexes, and...
