LLMs with Structured Output: A Quick Overview
LLMs may have been introduced to us through interfaces like ChatGPT. But many of our existing systems are not designed to accommodate the flexible, conversational outputs that LLMs typically produce. Instead, we need these models to be backward-compatible with our current infrastructure, where outputs must adhere to specific formats and schemas.
This is where structured output shines—it provides a method to generate outputs that are both precise and adaptable, easily integrating into the broader ecosystem of software tools.
Why Structured Outputs Matter
Structured output is an essential capability because it enables the generation of outputs that are not just free-form text but well-defined objects. These objects can be easily parsed and utilized by other software components, making them compatible with existing systems and workflows.
For instance, consider an application that processes meeting transcripts. Instead of producing a verbose summary that may require additional processing, a structured output would extract key event details and format them into a structured JSON object that is ready for database insertion. This approach reduces the need for additional validation and error handling, ensuring that the data received is in the expected format.
Tools for Structured Outputs
Recognizing the importance of structured outputs, OpenAI has recently introduced native support for structured outputs with support for JSON schema in REST APIs or object schemas using libraries like Pydantic and Zod. This support simplifies the process of generating structured outputs, allowing developers to more easily integrate LLMs into existing systems.
An alternative approach is provided by the Instructor library, which has been available for some time. The Instructor library allows developers to specify a Pydantic model to map the LLM output to a specific schema. The library is designed to work not only with OpenAI's API but also with other providers that have compatible interfaces, such as Anyscale, Ollama, Groq etc. This flexibility allows teams to derive structured data outputs from various LLMs without custom integration work
Benefits of Using Structured Outputs
The use of structured outputs offers several significant benefits:
Reliability and Type Safety: Adhering to a defined schema reduces the need for additional validation and error handling, ensuring data reliability.
Simplified Prompting: Developers can use less verbose prompts as the schema dictates the expected output format, streamlining interactions with the model.
Explicit Refusals: Applications can programmatically detect when a model refuses to provide an output, enhancing safety and control over the model’s responses.
By using structured outputs, developers can easily integrate LLMs into existing systems, making them more compatible, reliable, and efficient. If you’re only working with OpenAI models and need basic structured responses, OpenAI’s native structured outputs are the best choice—they’re convenient, secure, and affordable. But if you’re using other LLM providers or want to keep your code flexible across different platforms, tools like the Instructor library offer the versatility you need to fully leverage structured outputs.
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