Structured outputs with Amazon Nova: A guide for builders

Developers building AI applications face a common challenge: converting unstructured data into structured formats. Structured output is critical for machine-to-machine communication use cases, because this enables downstream use cases to more effectively consume and process the generated outputs. Whether it’s extracting information from documents, creating assistants that fetch data from APIs, or developing agents that take actions, these tasks require foundation models to generate outputs in specific structured formats.

We launched constrained decoding to provide reliability when using tools for structured outputs. Now, tools can be used with Amazon Nova foundation models (FMs) to extract data based on complex schemas, reducing tool use errors by over 95%.

In this post, we explore how you can use Amazon Nova FMs for structured output use cases.

Techniques for implementing structured outputs

When addressing the requirements for structured outputs use cases, there are two common approaches for implementation. You can modify the ...