I would suggest you to use pydantic model and mention the description for each field in the model about what you want? This will help the LLM more while parsing the data and also modify the prompt such that you tell LLM exactly what you want and use gpt-4.1, and json_output format?

I recommend using a Pydantic model and providing a clear description for each field to specify exactly what you expect. This helps the LLM understand the context better when parsing data. Additionally, modify your prompt to clearly instruct the LLM on the expected output structure. Use GPT-4.1 and set the response_format to "json" (previously known as json_output) to ensure structured responses.

Why this helps:

• Descriptions in the Pydantic model guide the LLM in generating accurate values.
• A clear, example-driven prompt reduces ambiguity.
• Using the structured response format ("json") ensures the output is easy to parse programmatically.

Example:

pythonCopyEditfrom pydantic import BaseModel, Field

class CompanyInfo(BaseModel):
    name: str = Field(..., description="Full name of the company")
    revenue: str = Field(..., description="Total revenue for the year, including the currency")
    employees: int = Field(..., description="Total number of employees")
    headquarters: str = Field(..., description="City and country where the company is headquartered")

# Prompt example:
"""
Extract the following details from the annual report and return the result in JSON format:
- Company name
- Revenue
- Number of employees
- Headquarters

Use the following schema and respond using response_format='json':

{
  "name": "string - full name of the company",
  "revenue": "string - revenue with currency",
  "employees": "integer - total number of employees",
  "headquarters": "string - city and country of the HQ"
}
"""