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What is Structured Generation

Structured generation refers to guiding a large language model (LLM) to produce output in a specific, structured format rather than open-ended free text. Instead of a long free-form paragraph, the model’s response is constrained to a predetermined schema or layout (for example, a JSON object with defined fields). This approach ensures the output is organized and machine-readable, making it easier for other systems to parse and use the information. In essence, the model is directed to fill in a template or data structure, which improves the consistency and reliability of its results.

from pydantic import BaseModel
from openai import OpenAI

client = OpenAI()

class ResearchPaperExtraction(BaseModel):
    title: str
    authors: list[str]
    abstract: str
    keywords: list[str]

completion = uiclient.completions.parse(
    json_schema=ResearchPaperExtraction.model_json_schema(),
    messages=[
        {"role": "system", "content": "You are an expert at structured data extraction. You will be given unstructured text from a research paper and should convert it into the given structure."},
        {"role": "user", "content": "..."}
    ],
    model="gpt-4.1",
    temperature=0
)

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How to Use Structured Generation

Modern LLM services have introduced native support for structured generation, making it easier to enforce specific output formats. For instance, OpenAI’s API now provides a “structured outputs” feature that guarantees the model’s reply will conform exactly to a developer-supplied JSON schema. Google’s Gemini API offers a similar capability: you can configure a response schema (for example, via a responseSchema parameter or a Pydantic model in code) so that the model returns JSON data in the expected shape. Even emerging platforms like xAI’s Grok model include structured output modes, allowing you to supply a schema or format hints to ensure the response comes back in a specific organized form.

Using these features typically involves defining a schema for the desired output and providing it as part of your API request. The schema can be a formal JSON Schema document or a data model class (such as a Pydantic BaseModel in Python) that the SDK will convert into a JSON Schema behind the scenes. The LLM will then generate a response conforming to that schema, populating the required fields and structure. This guided approach removes the guesswork from parsing model outputs – instead of writing brittle prompt instructions or doing heavy post-processing to get a well-formatted result, you receive a valid JSON (or other structured format) directly from the model.

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What UiForm Brings

UiForm is a platform designed to make structured generation easier and more reliable, helping users get started quickly. It builds on the native capabilities of LLM APIs and adds a user-friendly layer for preparation and evaluation. Key features of UiForm include:

  • Preprocessing: Automatically converts various file types (documents, spreadsheets, images, etc.) into text or image data that LLMs can interpret. This allows you to feed PDFs, Excel sheets, emails, or images into the model by turning them into an LLM-readable format, eliminating the need to write custom parsers or manual data conversion.
  • Schema UI: Provides an interactive interface to define the desired output schema without writing any code. You can visually specify the fields and data types for the model’s output (essentially designing the JSON structure or other format), even if you aren’t familiar with JSON Schema syntax. This user-friendly schema builder ensures that both technical and non-technical users can easily set up and enforce the expected output format.
  • Evaluation: Offers a spreadsheet-style evaluation dashboard to test and measure model outputs across many examples. You can run the model on a batch of test inputs and see all the outputs in a tabular view, making it easy to spot errors or deviations from the expected format. UiForm also tracks performance metrics (such as accuracy of extracted fields or JSON validity) over time, allowing you to monitor improvements or regressions as you refine your prompts or upgrade the model.

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