Science works with chunks and bits and pieces of things with the continuity presumed, and Art works only with the continuities of things with the chunks and bits and pieces presumed. – Robert M. Pirsig The ability of LLMs to produce structured outputs is important for downstream applications that rely on reliably parsing output values. Developers want to quickly turn results from an AI model into data types, such as JSON, XML or Java Classes, that can be passed to other functions and methods in their applications. The Spring AI Structured Output Converters help to convert the LLM output into a structured format. As shown in the following diagram, this approach operates around the LLM text completion endpoint: Generating structured outputs from Large Language Models (LLMs) using generic completion APIs requires careful handling of inputs and outputs. The structured output converter plays a crucial role both before and after the LLM call, ensuring that the desired output structure is achieved. Prior to the LLM call, the converter appends format instructions to the prompt, providing explicit guidance to the models on generating the desired output structure. These instructions act as a blueprint, shaping the model's response to conform to the specified format. Subsequent to the LLM call, the converter takes the model's output text and transforms it into instances of the structured type. This conversion process involves parsing the raw text output and mapping it to the…Read More